Deductive and abductive reasoning are two critical paradigms for analyzing knowledge graphs, enabling applications from financial query answering to scientific discovery. Deductive reasoning on knowledge graphs usually involves retrieving entities that satisfy a complex logical query, while abductive reasoning generates plausible logical hypotheses from observations. Despite their clear synergistic potential, where deduction can validate hypotheses and abduction can uncover deeper logical patterns, existing methods address them in isolation. To bridge this gap, we propose DARK, a unified framework for Deductive and Abductive Reasoning in Knowledge graphs. As a masked diffusion model capable of capturing the bidirectional relationship between queries and conclusions, DARK has two key innovations. First, to better leverage deduction for hypothesis refinement during abductive reasoning, we introduce a self-reflective denoising process that iteratively generates and validates candidate hypotheses against the observed conclusion. Second, to discover richer logical associations, we propose a logic-exploration reinforcement learning approach that simultaneously masks queries and conclusions, enabling the model to explore novel reasoning compositions. Extensive experiments on multiple benchmark knowledge graphs show that DARK achieves competitive performance on both deductive and abductive reasoning tasks, demonstrating the significant benefits of our unified approach.

The ''Frontiers in Graph Machine Learning for the Large Model Era (GMLLM'25)'' workshop focuses on advancing graph machine learning (GML) techniques in the context of increasingly large and powerful models. Graphs offer a principled way to represent structured and relational data, making them essential for capturing complex dependencies in knowledge, systems, and behaviors. As the scale and influence of foundation models grow, graph learning stands at a unique vantage point to enhance model robustness, improve interpretability, and integrate domain-specific relational priors. This workshop explores how graph learning can support emerging needs in knowledge reasoning, temporal and multi-hop inference, and AI systems. It also investigates how advances in representation learning, structure-aware generalization, and efficient graph processing can contribute to trustworthy and scalable AI systems. By convening experts in graph learning, knowledge management, and LLMs, the workshop aims to identify core challenges and opportunities of GML in the large model era.

To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge needs learning-based methods because they are capable of generalizing over unobserved knowledge. Therefore, an appropriate dataset is fundamental to both obtaining and evaluating such methods under this paradigm. In this paper, we propose a comprehensive framework for data generation, model training, and method evaluation that covers the combinatorial space of Existential First-order Queries with multiple variables (EFOk). The combinatorial query space in our framework significantly extends those defined by set operations in the existing literature. Additionally, we construct a dataset, EFOk-CQA, with 741 query types for empirical evaluation, and our benchmark results provide new insights into how query hardness affects the results. Furthermore, we demonstrate that the existing dataset construction process is systematically biased and hinders the appropriate development of query-answering methods, highlighting the importance of our work. Our code and data are provided in https://github.com/HKUST-KnowComp/EFOK-CQA.

Understanding user intentions is essential for improving product recommendations, navigation suggestions, and query reformulations. However, user intentions can be intricate, involving multiple sessions and attribute requirements connected by logical operators such as And, Or, and Not. For instance, a user may search for Nike or Adidas running shoes across various sessions, with a preference for purple. In another example, a user may have purchased a mattress in a previous session and is now looking for a matching bed frame without intending to buy another mattress. Existing research on session understanding has not adequately addressed making product or attribute recommendations for such complex intentions. In this paper, we present the task of logical session complex query answering (LS-CQA), where sessions are treated as hyperedges of items, and we frame the problem of complex intention understanding as an LS-CQA task on an aggregated hypergraph of sessions, items, and attributes. This is a unique complex query answering task with sessions as ordered hyperedges. We also introduce a new model, the Logical Session Graph Transformer (LSGT), which captures interactions among items across different sessions and their logical connections using a transformer structure. We analyze the expressiveness of LSGT and prove the permutation invariance of the inputs for the logical operators. By evaluating LSGT on three datasets, we demonstrate that it achieves state-of-the-art results.

In the era of large language models (LLMs), efficient and accurate data retrieval has become increasingly crucial for the use of domain-specific or private data in the retrieval augmented generation (RAG). Neural graph databases (NGDBs) have emerged as a powerful paradigm that combines the strengths of graph databases (GDBs) and neural networks to enable efficient storage, retrieval, and analysis of graph-structured data which can be adaptively trained with LLMs. The usage of neural embedding storage and Complex neural logical Query Answering (CQA) provides NGDBs with generalization ability. When the graph is incomplete, by extracting latent patterns and representations, neural graph databases can fill gaps in the graph structure, revealing hidden relationships and enabling accurate query answering. Nevertheless, this capability comes with inherent trade-offs, as it introduces additional privacy risks to the domain-specific or private databases. Malicious attackers can infer more sensitive information in the database using well-designed queries such as from the answer sets of where Turing Award winners born before 1950 and after 1940 lived, the living places of Turing Award winner Hinton are probably exposed, although the living places may have been deleted in the training stage due to the privacy concerns. In this work, we propose a privacy-preserved neural graph database (P-NGDB) framework to alleviate the risks of privacy leakage in NGDBs. We introduce adversarial training techniques in the training stage to enforce the NGDBs to generate indistinguishable answers when queried with private information, enhancing the difficulty of inferring sensitive information through combinations of multiple innocuous queries. Extensive experimental results on three datasets show that our framework can effectively protect private information in the graph database while delivering high-quality public answers responses to queries. The code is available at https://github.com/HKUST-KnowComp/PrivateNGDB.

Recommender systems can be privacy-sensitive. To protect users' private historical interactions, federated learning has been proposed in distributed learning for user representations. Using federated recommender (FedRec) systems, users can train a shared recommendation model on local devices and prevent raw data transmissions and collections. However, the recommendation model learned by a common FedRec may still be vulnerable to private information leakage risks, particularly attribute inference attacks, which means that the attacker can easily infer users' personal attributes from the learned model. Additionally, traditional FedRecs seldom consider the diverse privacy preference of users, leading to difficulties in balancing the recommendation utility and privacy preservation. Consequently, FedRecs may suffer from unnecessary recommendation performance loss due to over-protection and private information leakage simultaneously. In this work, we propose a novel user-consented federated recommendation system (UC-FedRec) to flexibly satisfy the different privacy needs of users by paying a minimum recommendation accuracy price. UC-FedRec allows users to self-define their privacy preferences to meet various demands and makes recommendations with user consent. Experiments conducted on different real-world datasets demonstrate that our framework is more efficient and flexible compared to baselines. Our code is available at https://github.com/HKUST-KnowComp/UC-FedRec.

Learning graph embeddings is a crucial task in graph mining tasks. An effective graph embedding model can learn low-dimensional representations from graph-structured data for data publishing benefiting various downstream applications such as node classification, link prediction, etc. However, recent studies have revealed that graph embeddings are susceptible to attribute inference attacks, which allow attackers to infer private node attributes from the learned graph embeddings. To address these concerns, privacy-preserving graph embedding methods have emerged, aiming to simultaneously consider primary learning and privacy protection through adversarial learning. However, most existing methods assume that representation models have access to all sensitive attributes in advance during the training stage, which is not always the case due to diverse privacy preferences. Furthermore, the commonly used adversarial learning technique in privacy-preserving representation learning suffers from unstable training issues. In this paper, we propose a novel approach calledPrivate Variational Graph AutoEncoders (PVGAE) with the aid of independent distribution penalty as a regularization term. Specifically, we split the original variational graph autoencoder (VGAE) to learn sensitive and non-sensitive latent representations using two sets of encoders. Additionally, we introduce a novel regularization to enforce the independence of the encoders. We prove the theoretical effectiveness of regularization from the perspective of mutual information. Experimental results on three real-world datasets demonstrate that PVGAE outperforms other baselines in private embedding learning regarding utility performance and privacy protection.

A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based applications, such as dialogue systems and interactive search engines, rely on the ability to answer complex logic queries as a fundamental task. In most knowledge graphs, edges are typically used to either describe the relationships between entities or their associated attribute values. An attribute value can be in categorical or numerical format, such as dates, years, sizes, etc. However, existing complex query answering (CQA) methods simply treat numerical values in the same way as they treat entities. This can lead to difficulties in answering certain queries, such as which Australian Pulitzer award winner is born before 1927, and which drug is a pain reliever and has fewer side effects than Paracetamol. In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. In this task, we introduce new numerical variables and operations to describe queries involving numerical attribute values. To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures. During the numerical encoding process, NRN employs a parameterized density function to encode the distribution of numerical values. During the entity encoding process, NRN uses established query encoding methods for the original CQA problem. Experimental results show that NRN consistently improves various query encoding methods on three different knowledge graphs and achieves state-of-the-art results.

Query Parsing aims to extract product attributes, such as color, brand, and product type, from search queries. These attributes play a crucial role in search engines for tasks such as matching, ranking, and recommendation. There are two types of attributes: explicit attributes that are mentioned explicitly in the search query, and implicit attributes that are mentioned implicitly. Existing works on query parsing do not differentiate between explicit query parsing and implicit query parsing, which limits their performance in product search engines. In this work, we demonstrate the critical importance of implicit attributes in real-world product search engines. We then present our solution for implicit query parsing at Amazon Search, which is a unified framework combining recent advancements in knowledge graph technologies and customer behavior analysis. We demonstrate the effectiveness of our proposal through offline experiments on Amazon search log data. We also show how to deploy and use the framework on Amazon search to improve customers' shopping experiences.

Fine-tuning can be vulnerable to adversarial attacks. Existing works about black-box attacks on fine-tuned models (BAFT) are limited by strong assumptions. To fill the gap, we propose two novel BAFT settings, cross-domain and cross-domain cross-architecture BAFT, which only assume that (1) the target model for attacking is a fine-tuned model, and (2) the source domain data is known and accessible. To successfully attack fine-tuned models under both settings, we propose to first train an adversarial generator against the source model, which adopts an encoder-decoder architecture and maps a clean input to an adversarial example. Then we search in the low-dimensional latent space produced by the encoder of the adversarial generator. The search is conducted under the guidance of the surrogate gradient obtained from the source model. Experimental results on different domains and different network architectures demonstrate that the proposed attack method can effectively and efficiently attack the fine-tuned models.

LIA

No abstract available.

Various data mining tasks have been proposed to study Community Question Answering (CQA) platforms like Stack Overflow. The relatedness between some of these tasks provides useful learning signals to each other via Multi-Task Learning (MTL). However, due to the high heterogeneity of these tasks, few existing works manage to jointly solve them in a unified framework. To tackle this challenge, we develop a multi-relational graph based MTL model called Heterogeneous Multi-Task Graph Isomorphism Network (HMTGIN) which efficiently solves heterogeneous CQA tasks. In each training forward pass, HMTGIN embeds the input CQA forum graph by an extension of Graph Isomorphism Network and skip connections. The embeddings are then shared across all task-specific output layers to compute respective losses. Moreover, two cross-task constraints based on the domain knowledge about tasks' relationships are used to regularize the joint learning. In the evaluation, the embeddings are shared among different task-specific output layers to make corresponding predictions. To the best of our knowledge, HMTGIN is the first MTL model capable of tackling CQA tasks from the aspect of multi-relational graphs. To evaluate HMTGIN's effectiveness, we build a novel large-scale multi-relational graph CQA dataset with over two million nodes from Stack Overflow. Extensive experiments show that: (1) HMTGIN is superior to all baselines on five tasks; (2) The proposed MTL strategy and cross-task constraints have substantial advantages.

Knowledge graph embedding plays an important role in knowledge representation, reasoning, and data mining applications. However, for multiple cross-domain knowledge graphs, state-of-the-art embedding models cannot make full use of the data from different knowledge domains while preserving the privacy of exchanged data. In addition, the centralized embedding model may not scale to the extensive real-world knowledge graphs. Therefore, we propose a novel decentralized scalable learning framework, Federated Knowledge Graphs Embedding (FKGE), where embeddings from different knowledge graphs can be learnt in an asynchronous and peer-to-peer manner while being privacy-preserving. FKGE exploits adversarial generation between pairs of knowledge graphs to translate identical entities and relations of different domains into near embedding spaces. In order to protect the privacy of the training data, FKGE further implements a privacy-preserving neural network structure to guarantee no raw data leakage. We conduct extensive experiments to evaluate FKGE on 11 knowledge graphs, demonstrating a significant and consistent improvement in model quality with at most 17.85% and 7.90% increases in performance on triple classification and link prediction tasks.

PathSim is a widely used meta-path-based similarity in heterogeneous information networks. Numerous applications rely on the computation of PathSim, including similarity search and clustering. Computing PathSim scores on large graphs is computationally challenging due to its high time and storage complexity. In this paper, we propose to transform the problem of approximating the ground truth PathSim scores into a learning problem. We design an encoder-decoder based framework, NeuPath, where the algorithmic structure of PathSim is considered. Specifically, the encoder module identifies Top T optimized path instances, which can approximate the ground truth PathSim, and maps each path instance to an embedding vector. The decoder transforms each embedding vector into a scalar respectively, which identifies the similarity score. We perform extensive experiments on two real-world datasets in different domains, ACM and IMDB. Our results demonstrate that NeuPath performs better than state-of-the-art baselines in the PathSim approximation task and similarity search task.

Commonsense knowledge is crucial for artificial intelligence systems to understand natural language. Previous commonsense knowledge acquisition approaches typically rely on human annotations (for example, ATOMIC) or text generation models (for example, COMET.) Human annotation could provide high-quality commonsense knowledge, yet its high cost often results in relatively small scale and low coverage. On the other hand, generation models have the potential to automatically generate more knowledge. Nonetheless, machine learning models often fit the training data well and thus struggle to generate high-quality novel knowledge. To address the limitations of previous approaches, in this paper, we propose an alternative commonsense knowledge acquisition framework DISCOS (from DIScourse to COmmonSense), which automatically populates expensive complex commonsense knowledge to more affordable linguistic knowledge resources. Experiments demonstrate that we can successfully convert discourse knowledge about eventualities from ASER, a large-scale discourse knowledge graph, into if-then commonsense knowledge defined in ATOMIC without any additional annotation effort. Further study suggests that DISCOS significantly outperforms previous supervised approaches in terms of novelty and diversity with comparable quality. In total, we can acquire 3.4M ATOMIC-like inferential commonsense knowledge by populating ATOMIC on the core part of ASER. Codes and data are available at https://github.com/HKUST-KnowComp/DISCOS-commonsense.

Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text information regarding public events with different topics. However, mining social events is challenging because events typically exhibit heterogeneous texture and metadata are often ambiguous. In this article, we first design a novel event-based meta-schema to characterize the semantic relatedness of social events and then build an event-based heterogeneous information network (HIN) integrating information from external knowledge base. Second, we propose a novel Pairwise Popularity Graph Convolutional Network, named as PP-GCN, based on weighted meta-path instance similarity and textual semantic representation as inputs, to perform fine-grained social event categorization and learn the optimal weights of meta-paths in different tasks. Third, we propose a streaming social event detection and evolution discovery framework for HINs based on meta-path similarity search, historical information about meta-paths, and heterogeneous DBSCAN clustering method. Comprehensive experiments on real-world streaming social text data are conducted to compare various social event detection and evolution discovery algorithms. Experimental results demonstrate that our proposed framework outperforms other alternative social event detection and evolution discovery techniques.

Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.

PageRank has been widely used to measure the authority or the influence of a user in social networks. However, conventional PageRank only makes use of edge-based relations, which represent first-order relations between two connected nodes. It ignores higher-order relations that may exist between nodes. In this article, we propose a novel framework, motif-based PageRank (MPR), to incorporate higher-order relations into the conventional PageRank computation. Motifs are subgraphs consisting of a small number of nodes. We use motifs to capture higher-order relations between nodes in a network and introduce two methods, one linear and one non-linear, to combine first-order and higher-order relations in PageRank computation. We conduct extensive experiments on three real-world networks, namely, DBLP, Epinions, and Ciao. We study different types of motifs, including 3-node simple and anchor motifs, 4-node and 5-node motifs. Besides using single motif, we also run MPR with ensemble of multiple motifs. We also design a learning task to evaluate the abilities of authority prediction with motif-based features. All experimental results demonstrate that MPR can significantly improve the performance of user ranking in social networks compared to the baseline methods.

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online question pools. In this paper, we propose a novel approach using Graph Neural Networks (GNNs) to achieve better student performance prediction in interactive online question pools. Specifically, we model the relationship between students and questions using student interactions to construct the student-interaction-question network and further present a new GNN model, called R2GCN, which intrinsically works for the heterogeneous networks, to achieve generalizable student performance prediction in interactive online question pools. We evaluate the effectiveness of our approach on a real-world dataset consisting of 104,113 mouse trajectories generated in the problem-solving process of over 4,000 students on 1,631 questions. The experiment results show that our approach can achieve a much higher accuracy of student performance prediction than both traditional machine learning approaches and GNN models.

In this paper, we study a new graph learning problem: learning to count subgraph isomorphisms. Different from other traditional graph learning problems such as node classification and link prediction, subgraph isomorphism counting is NP-complete and requires more global inference to oversee the whole graph. To make it scalable for large-scale graphs and patterns, we propose a learning framework that augments different representation learning architectures and iteratively attends pattern and target data graphs to memorize intermediate states of subgraph isomorphism searching for global counting. We develop both small graphs (<= 1,024 subgraph isomorphisms in each) and large graphs (<= 4,096 subgraph isomorphisms in each) sets to evaluate different representation and interaction modules. A mutagenic compound dataset, MUTAG, is also used to evaluate neural models and demonstrate the success of transfer learning. While the learning based approach is inexact, we are able to generalize to count large patterns and data graphs in linear time compared to the exponential time of the original NP-complete problem. Experimental results show that learning based subgraph isomorphism counting can speed up the traditional algorithm, VF2, 10-1,000 times with acceptable errors. Domain adaptation based on fine-tuning also shows the usefulness of our approach in real-world applications.

Graph is a natural representation encoding both the features of the data samples and relationships among them. Analysis with graphs is a classic topic in data mining and many techniques have been proposed in the past. In recent years, because of the rapid development of data mining and knowledge discovery, many novel graph analytics algorithms have been proposed and successfully applied in a variety of areas. The goal of this tutorial is to summarize the graph analytics algorithms developed recently and how they have been applied in healthcare. In particular, our tutorial will cover both the technical advances and the application in healthcare. On the technical aspect, we will introduce deep network embedding techniques, graph neural networks, knowledge graph construction and inference, graph generative models and graph neural ordinary differential equation models. On the healthcare side, we will introduce how these methods can be applied in predictive modeling of clinical risks (e.g., chronic disease onset, in-hospital mortality, condition exacerbation, etc.) and disease subtyping with multi-modal patient data (e.g., electronic health records, medical image and multi-omics), knowledge discovery from biomedical literature and integration with data-driven models, as well as pharmaceutical research and development (e.g., de-novo chemical compound design and optimization, patient similarity for clinical trial recruitment and pharmacovigilance). We will conclude the whole tutorial with a set of potential issues and challenges such as interpretability, fairness and security. In particular, considering the global pandemic of COVID-19, we will also summarize the existing research that have already leveraged graph analytics to help with the understanding the mechanism, transmission, treatment and prevention of COVID-19, as well as point out the available resources and potential opportunities for future research.

Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adversarial attacks. Though transfer learning has been widely applied, its effect on model robustness is unclear. To figure out this problem, we conduct extensive empirical evaluations to show that fine-tuning effectively enhances model robustness under white-box FGSM attacks. We also propose a black-box attack method for transfer learning models which attacks the target model with the adversarial examples produced by its source model. To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model. Empirical results show that the adversarial examples are more transferable when fine-tuning is used than they are when the two networks are trained independently.

In this paper, we study the fundamental problem of random walk for network embedding. We propose to use non-Markovian random walk, variants of vertex-reinforced random walk (VRRW), to fully use the history of a random walk path. To solve the getting stuck problem of VRRW, we introduce an exploitation-exploration mechanism to help the random walk jump out of the stuck set. The new random walk algorithms share the same convergence property of VRRW and thus can be used to learn stable network embeddings. Experimental results on two link prediction benchmark datasets and three node classification benchmark datasets show that our proposed approach reinforce2vec can outperform state-of-the-art random walk based embedding methods by a large margin.

Deep domain adaptation models learn a neural network in an unlabeled target domain by leveraging the knowledge from a labeled source domain. This can be achieved by learning a domain-invariant feature space. Though the learned representations are separable in the source domain, they usually have a large variance and samples with different class labels tend to overlap in the target domain, which yields suboptimal adaptation performance. To fill the gap, a Fisher loss is proposed to learn discriminative representations which are within-class compact and between-class separable. Experimental results on two benchmark datasets show that the Fisher loss is a general and effective loss for deep domain adaptation. Noticeable improvements are brought when it is used together with widely adopted transfer criteria, including MMD, CORAL and domain adversarial loss. For example, an absolute improvement of 6.67% in terms of the mean accuracy is attained when the Fisher loss is used together with the domain adversarial loss on the Office-Home dataset.

Understanding human’s language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge about activities, states, or events, which are used to describe how entities or things act in the real world. To fill this gap, we develop ASER (activities, states, events, and their relations), a large-scale eventuality knowledge graph extracted from more than 11-billion-token unstructured textual data. ASER contains 15 relation types belonging to five categories, 194-million unique eventualities, and 64-million unique edges among them. Both intrinsic and extrinsic evaluations demonstrate the quality and effectiveness of ASER.

Network representation learning has aroused widespread interests in recent years. While most of the existing methods deal with edges as pairwise relationships, only a few studies have been proposed for hyper-networks to capture more complicated tuplewise relationships among multiple nodes. A hyper-network is a network where each edge, called hyperedge, connects an arbitrary number of nodes. Different from conventional networks, hyper-networks have certain degrees of indecomposability such that the nodes in a subset of a hyperedge may not possess a strong relationship. That is the main reason why traditional algorithms fail in learning representations in hyper-networks by simply decomposing hyperedges into pairwise relationships. In this paper, we firstly define a metric to depict the degrees of indecomposability for hyper-networks. Then we propose a new concept called hyper-path and design hyper-path-based random walks to preserve the structural information of hyper-networks according to the analysis of the indecomposability. Then a carefully designed algorithm, Hyper-gram, utilizes these random walks to capture both pairwise relationships and tuplewise relationships in the whole hyper-networks. Finally, we conduct extensive experiments on several real-world datasets covering the tasks of link prediction and hyper-network reconstruction, and results demonstrate the rationality, validity, and effectiveness of our methods compared with those existing state-of-the-art models designed for conventional networks or hyper-networks.

Heterogeneous information network (HIN) embedding has gained increasing interests recently. However, the current way of random-walk based HIN embedding methods have paid few attention to the higher-order Markov chain nature of meta-path guided random walks, especially to the stationarity issue. In this paper, we systematically formalize the meta-path guided random walk as a higher-order Markov chain process,and present a heterogeneous personalized spacey random walk to efficiently and effectively attain the expected stationary distribution among nodes. Then we propose a generalized scalable framework to leverage the heterogeneous personalized spacey random walk to learn embeddings for multiple types of nodes in an HIN guided by a meta-path, a meta-graph, and a meta-schema respectively. We conduct extensive experiments in several heterogeneous networks and demonstrate that our methods substantially outperform the existing state-of-the-art network embedding algorithms.

Heterogeneous Information Networks (HIN) has been widely used in recommender systems (RSs). In previous HIN-based RSs, meta-path is used to compute the similarity between users and items. However, existing meta-path based methods only consider first-order relations, ignoring higher-order relations among the nodes ofsame type, captured bymotifs. In this paper, we propose to use motifs to capture higher-order relations among nodes of same type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine motif-based higher-order relations with edge-based first-order relations. With MEMP-based similarities between users and items, we design a recommending model MoHINRec, and experimental results on two real-world datasets, Epinions and CiaoDVD, demonstrate its superiority over existing HIN-based RS methods.

An effective content recommendation in modern social media platforms should benefit both creators to bring genuine benefits to them and consumers to help them get really interesting content. In this paper, we propose a model called Social Explorative Attention Network (SEAN) for content recommendation. SEAN uses a personalized content recommendation model to encourage personal interests driven recommendation. Moreover, SEAN allows the personalization factors to attend to users' higher-order friends on the social network to improve the accuracy and diversity of recommendation results. Constructing two datasets from a popular decentralized content distribution platform, Steemit, we compare SEAN with state-of-the-art CF and content based recommendation approaches. Experimental results demonstrate the effectiveness of SEAN in terms of both Gini coefficients for recommendation equality and F1 scores for recommendation performance.

Hierarchical text classification has many real-world applications. However, labeling a large number of documents is costly. In practice, we can use semi-supervised learning or weakly supervised learning (e.g., dataless classification) to reduce the labeling cost. In this paper, we propose a path cost-sensitive learning algorithm to utilize the structural information and further make use of unlabeled and weakly-labeled data. We use a generative model to leverage the large amount of unlabeled data and introduce path constraints into the learning algorithm to incorporate the structural information of the class hierarchy. The posterior probabilities of both unlabeled and weakly labeled data can be incorporated with path-dependent scores. Since we put a structure-sensitive cost to the learning algorithm to constrain the classification consistent with the class hierarchy and do not need to reconstruct the feature vectors for different structures, we can significantly reduce the computational cost compared to structural output learning. Experimental results on two hierarchical text classification benchmarks show that our approach is not only effective but also efficient to handle the semi-supervised and weakly supervised hierarchical text classification.

Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bag-of-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.

Knowledge acquisition (e.g. through labeling) is one of the most successful applications in crowdsourcing. In practice, collecting as specific as possible knowledge via crowdsourcing is very useful since specific knowledge can be generalized easily if we have a knowledge base, but it is difficult to infer specific knowledge from general knowledge. Meanwhile, tasks for acquiring more specific knowledge can be more difficult for workers, thus need more answers to infer high-quality results. Given a limited budget, assigning workers to difficult tasks will be more effective for the goal of specific knowledge acquisition. However, existing crowdsourcing task scheduling cannot incorporate the specificity of workers' answers. In this paper, we present a new framework for task scheduling with the limited budget, targeting an effective solution to more specific knowledge acquisition. We propose novel criteria for evaluating the quality of specificity-dependent answers and result inference algorithms to aggregate more specific answers with budget constraints. We have implemented our framework with real crowdsourcing data and platform, and have achieved significant performance improvement compared with existing approaches.

Measuring network similarity is a fundamental data mining problem. The mainstream similarity measures mainly leverage the structural information regarding to the entities in the network without considering the network semantics. In the real world, the heterogeneous information networks (HINs) with rich semantics are ubiquitous. However, the existing network similarity doesn't generalize well in HINs because they fail to capture the HIN semantics. The meta-path has been proposed and demonstrated as a right way to represent semantics in HINs. Therefore, original meta-path based similarities (e.g., PathSim and KnowSim) have been successful in computing the entity proximity in HINs. The intuition is that the more instances of meta-path(s) between entities, the more similar the entities are. Thus the original meta-path similarity only applies to computing the proximity of two neighborhood (connected) entities. In this paper, we propose the distant meta-path similarity that is able to capture HIN semantics between two distant (isolated) entities to provide more meaningful entity proximity. The main idea is that even there is no shared neighborhood entities of (i.e., no meta-path instances connecting) the two entities, but if the more similar neighborhood entities of the entities are, the more similar the two entities should be. We then find out the optimum distant meta-path similarity by exploring the similarity hypothesis space based on different theoretical foundations. We show the state-of-the-art similarity performance of distant meta-path similarity on two text-based HINs and make the datasets public available.

With explosive growth of Android malware and due to the severity of its damages to smart phone users, the detection of Android malware has become increasingly important in cybersecurity. The increasing sophistication of Android malware calls for new defensive techniques that are capable against novel threats and harder to evade. In this paper, to detect Android malware, instead of using Application Programming Interface (API) calls only, we further analyze the different relationships between them and create higher-level semantics which require more effort for attackers to evade the detection. We represent the Android applications (apps), related APIs, and their rich relationships as a structured heterogeneous information network (HIN). Then we use a meta-path based approach to characterize the semantic relatedness of apps and APIs. We use each meta-path to formulate a similarity measure over Android apps, and aggregate different similarities using multi-kernel learning. Then each meta-path is automatically weighted by the learning algorithm to make predictions. To the best of our knowledge, this is the first work to use structured HIN for Android malware detection. Comprehensive experiments on real sample collections from Comodo Cloud Security Center are conducted to compare various malware detection approaches. Promising experimental results demonstrate that our developed system HinDroid outperforms other alternative Android malware detection techniques.

Heterogeneous Information Network (HIN) is a natural and general representation of data in modern large commercial recommender systems which involve heterogeneous types of data. HIN based recommenders face two problems: how to represent the high-level semantics of recommendations and how to fuse the heterogeneous information to make recommendations. In this paper, we solve the two problems by first introducing the concept of meta-graph to HIN-based recommendation, and then solving the information fusion problem with a "matrix factorization (MF) + factorization machine (FM)" approach. For the similarities generated by each meta-graph, we perform standard MF to generate latent features for both users and items. With different meta-graph based features, we propose to use FM with Group lasso (FMG) to automatically learn from the observed ratings to effectively select useful meta-graph based features. Experimental results on two real-world datasets, Amazon and Yelp, show the effectiveness of our approach compared to state-of-the-art FM and other HIN-based recommendation algorithms.

Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and mapped precisely to structured queries over the knowledge base. The challenge, however, is that a human can ask one question in many different ways. Previous approaches have natural limits due to their representations: rule based approaches only understand a small set of "canned" questions, while keyword based or synonym based approaches cannot fully understand the questions. In this paper, we design a new kind of question representation: templates , over a billion scale knowledge base and a million scale QA corpora. For example, for questions about a city's population, we learn templates such as What's the population of $city?, How many people are there in $city?. We learned 27 million templates for 2782 intents. Based on these templates, our QA system KBQA effectively supports binary factoid questions, as well as complex questions which are composed of a series of binary factoid questions. Furthermore, we expand predicates in RDF knowledge base, which boosts the coverage of knowledge base by 57 times. Our QA system beats all other state-of-art works on both effectiveness and efficiency over QALD benchmarks.

Recent studies have demonstrated the power of modeling real world data as heterogeneous information networks (HINs) consisting of multiple types of entities and relations. Unfortunately, most of such studies (e.g., similarity search) confine discussions on the networks with only a few entity and relationship types, such as DBLP. In the real world, however, the network schema can be rather complex, such as Freebase. In such HINs with rich schema, it is often too much burden to ask users to provide explicit guidance in selecting relations for similarity search. In this paper, we study the problem of relation similarity search in schema-rich HINs. Under our problem setting, users are only asked to provide some simple relation instance examples (e.g., 〈Barack Obama, John Kerry〉 and 〈George W. Bush, Condoleezza Rice〉) as a query, and we automatically detect the latent semantic relation (LSR) implied by the query (e.g., “president vs. secretary-of-state”). Such LSR will help to find other similar relation instances (e.g., 〈Bill Clinton, Madeleine Albright〉). In order to solve the problem, we first define a new meta-path-based relation similarity measure, RelSim, to measure the similarity between relation instances in schema-rich HINs. Then given a query, we propose an optimization model to efficiently learn LSR implied in the query through linear programming, and perform fast relation similarity search using RelSim based on the learned LSR. The experiments on real world datasets derived from Freebase demonstrate the effectiveness and efficiency of our approach.

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then, the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this article, we provide an example of using world knowledge for domain-dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then, we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, WordNet. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features. A preliminary version of this work appeared in the proceedings of KDD 2015 [Wang et al. 2015a]. This journal version has made several major improvements. First, we have proposed a new and general learning framework for machine learning with world knowledge as indirect supervision, where document clustering is a special case in the original paper. Second, in order to make our unsupervised semantic parsing method more understandable, we add several real cases from the original sentences to the resulting logic forms with all the necessary information. Third, we add details of the three semantic filtering methods and conduct deep analysis of the three semantic filters, by using case studies to show why the conceptualization-based semantic filter can produce more accurate indirect supervision. Finally, in addition to the experiment on 20 newsgroup data and Freebase, we have extended the experiments on clustering results by using all the combinations of text (20 newsgroup, MCAT, CCAT, ECAT) and world knowledge sources (Freebase, YAGO2).

Multi-modal similarity search has attracted considerable attention to meet the need of information retrieval across different types of media. To enable efficient multi-modal similarity search in large-scale databases recently, researchers start to study multi-modal hashing. Most of the existing methods are applied to search across multi-views among which explicit correspondence is provided. Given a multi-modal similarity search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH provides more flexible and discriminative ability by embedding heterogeneous media into different Hamming spaces, compared to almost all existing methods that map heterogeneous data in a common Hamming space. We formulate a joint optimization model to learn hash functions embedding heterogeneous media into different Hamming spaces, and a translator aligning different Hamming spaces. The extensive experiments on two real-world datasets, one publicly available dataset of Flickr, and the other MIRFLICKR-Yahoo Answers dataset, highlight the effectiveness and efficiency of our algorithm.

As a fundamental task, document similarity measure has broad impact to document-based classification, clustering and ranking. Traditional approaches represent documents as bag-of-words and compute document similarities using measures like cosine, Jaccard, and dice. However, entity phrases rather than single words in documents can be critical for evaluating document relatedness. Moreover, types of entities and links between entities/words are also informative. We propose a method to represent a document as a typed heterogeneous information network (HIN), where the entities and relations are annotated with types. Multiple documents can be linked by the words and entities in the HIN. Consequently, we convert the document similarity problem to a graph distance problem. Intuitively, there could be multiple paths between a pair of documents. We propose to use the meta-path defined in HIN to compute distance between documents. Instead of burdening user to define meaningful meta-paths, an automatic method is proposed to rank the meta-paths. Given the meta-paths associated with ranking scores, an HIN-based similarity measure, KnowSim, is proposed to compute document similarities. Using Freebase, a well-known world knowledge base, to conduct semantic parsing and construct HIN for documents, our experiments on 20Newsgroups and RCV1 datasets show that KnowSim generates impressive high-quality document clustering.

Cross-domain recommendation has attracted wide research interest which generally aims at improving the recommendation performance by alleviating the cold start problem in collaborative filtering based recommendation or generating a more comprehensive user profiles from multiple domains. In most previous cross-domain recommendation settings, explicit or implicit relationships can be easily established across different domains. However, many real applications belong to a more challenging setting: recommendation across heterogeneous domains without explicit relationships, where neither explicit user-item relations nor overlapping features exist between different domains. In this new setting, we need to (1) enrich the sparse data to characterize users or items and (2) bridge the gap caused by the heterogenous features in different domains. To overcome the first challenge, we proposed an optimized local tag propagation algorithm to generate descriptive tags for user profiling. For the second challenge, we proposed a semantic relatedness metric by mapping the heterogenous features onto their concept space derived from online encyclopedias. We conducted extensive experiments on two real datasets to justify the effectiveness of our solution.

Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related. If there is little overlap between the two domains, performing knowledge transfer between these domains will not be effective. Inspired by human transitive inference and learning ability, whereby two seemingly unrelated concepts can be connected by a string of intermediate bridges using auxiliary concepts, in this paper we study a novel learning problem: Transitive Transfer Learning (abbreviated to TTL). TTL is aimed at breaking the large domain distances and transfer knowledge even when the source and target domains share few factors directly. For example, when the source and target domains are documents and images respectively, TTL could use some annotated images as the intermediate domain to bridge them. To solve the TTL problem, we propose a learning framework to mimic the human learning process. The framework is composed of an intermediate domain selection component and a knowledge transfer component. Extensive empirical evidence shows that the framework yields state-of-the-art classification accuracies on several classification data sets.

One of the key obstacles in making learning protocols realistic in applications is the need to supervise them, a costly process that often requires hiring domain experts. We consider the framework to use the world knowledge as indirect supervision. World knowledge is general-purpose knowledge, which is not designed for any specific domain. Then the key challenges are how to adapt the world knowledge to domains and how to represent it for learning. In this paper, we provide an example of using world knowledge for domain dependent document clustering. We provide three ways to specify the world knowledge to domains by resolving the ambiguity of the entities and their types, and represent the data with world knowledge as a heterogeneous information network. Then we propose a clustering algorithm that can cluster multiple types and incorporate the sub-type information as constraints. In the experiments, we use two existing knowledge bases as our sources of world knowledge. One is Freebase, which is collaboratively collected knowledge about entities and their organizations. The other is YAGO2, a knowledge base automatically extracted from Wikipedia and maps knowledge to the linguistic knowledge base, Word-Net. Experimental results on two text benchmark datasets (20newsgroups and RCV1) show that incorporating world knowledge as indirect supervision can significantly outperform the state-of-the-art clustering algorithms as well as clustering algorithms enhanced with world knowledge features.

We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns over time. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and the smoothness between trees. The state-of-the-art multi-branch clustering method, Bayesian rose trees, is employed to generate a topic tree with a high fitness value. A constraint model is also introduced to preserve the smoothness between trees. A set of comprehensive experiments on real world news data demonstrates that the proposed method better incorporates historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm. In contrast to our previous method[31], we implement two additional baseline algorithms to compare them with our algorithm. We also evaluate the performance of the clustering algorithm based on multiple constraint trees. Furthermore, two case studies are conducted to demonstrate the effectiveness and usefulness of our algorithm in helping users understand the major hierarchical topic evolutionary patterns in text data.

In this paper, we study a challenging problem of deriving a taxonomy from a set of keyword phrases. A solution can benefit many real-world applications because i) keywords give users the flexibility and ease to characterize a specific domain; and ii) in many applications, such as online advertisements, the domain of interest is already represented by a set of keywords. However, it is impossible to create a taxonomy out of a keyword set itself. We argue that additional knowledge and context are needed. To this end, we first use a general-purpose knowledgebase and keyword search to supply the required knowledge and context. Then, we develop a Bayesian approach to build a hierarchical taxonomy for a given set of keywords. We reduce the complexity of previous hierarchical clustering approaches from$O(n^2\; \log\; n)$to$O(n\; \log\; n)$using a nearest-neighbor-based approximation, so that we can derive a domain-specific taxonomy from one million keyword phrases in less than an hour. Finally, we conduct comprehensive large scale experiments to show the effectiveness and efficiency of our approach. A real life example of building an insurance-related web search query taxonomy illustrates the usefulness of our approach for specific domains.

One of the biggest challenges of commercial search engines is how to handle tail queries, or queries that occur very infrequently. Frequent queries, also known as head queries, are easier to handle largely because their intents are evidenced by abundant click-through data (query logs). Tail queries have little historical data to rely on, which makes them difficult to be learned by ranking algorithms. In this paper, we leverage knowledge from two resources to fill the gap. The first is a general knowledgebase containing different granularities of concepts automatically harnessed from the Web. The second is the click-through data for head queries. From the click-through data, we obtain an understanding of queries that trigger clicks. Then, we show that by extracting single or multi-word expressions from both head and tail queries and mapping them to a common concept space defined by the knowledgebase, we are able to transfer the click information of the head queries to the tail queries. To validate our approach, we conduct large scale experiments on two real data sets. One is a mixture of head and tail queries, and the other contains pure tail queries. We show that our approach effectively improves tail query search relevance.

Hashing has enjoyed a great success in large-scale similarity search. Recently, researchers have studied the multi-modal hashing to meet the need of similarity search across different types of media. However, most of the existing methods are applied to search across multi-views among which explicit bridge information is provided. Given a heterogeneous media search task, we observe that abundant multi-view data can be found on the Web which can serve as an auxiliary bridge. In this paper, we propose a Heterogeneous Translated Hashing (HTH) method with such auxiliary bridge incorporated not only to improve current multi-view search but also to enable similarity search across heterogeneous media which have no direct correspondence. HTH simultaneously learns hash functions embedding heterogeneous media into different Hamming spaces, and translators aligning these spaces. Unlike almost all existing methods that map heterogeneous data in a common Hamming space, mapping to different spaces provides more flexible and discriminative ability. We empirically verify the effectiveness and efficiency of our algorithm on two real world large datasets, one publicly available dataset of Flickr and the other MIRFLICKR-Yahoo Answers dataset.

Correlated topical trend detection is very useful in analyzing public and social media influence. In this paper, we propose an algorithm that can both detect the correlation and discover the corresponding keywords that trigger the correlation. To detect the correlation, we use a projection vector to project two text streams onto the same space, and then use a least square cost function to regress one text stream over the other with different time lags. To extract the corresponding keywords, we impose the non-negative sparsity constraints over the projection parameters. In addition, we present an accelerated algorithm based on Nesterov's method to efficiently solve the optimization problem. In our experiments, we use both syntehtic and real data sets to demonstrate the advantages and capabilities of the proposed algorithm over CCA on the follower link prediction problem.

Users' daily activities, such as dining and shopping, inherently reflect their habits, intents and preferences, thus provide invaluable information for services such as personalized information recommendation and targeted advertising. Users' activity information, although ubiquitous on social media, has largely been unexploited. This paper addresses the task of user activity classification in microblogs, where users can publish short messages and maintain social networks online. We identify the importance of modeling a user's individuality, and that of exploiting opinions of the user's friends for accurate activity classification. In this light, we propose a novel collaborative boosting framework comprising a text-to-activity classifier for each user, and a mechanism for collaboration between classifiers of users having social connections. The collaboration between two classifiers includes exchanging their own training instances and their dynamically changing labeling decisions. We propose an iterative learning procedure that is formulated as gradient descent in learning function space, while opinion exchange between classifiers is implemented with a weighted voting in each learning iteration. We show through experiments that on real-world data from Sina Weibo, our method outperforms existing off-the-shelf algorithms that do not take users' individuality or social connections into account.

Understanding topic hierarchies in text streams and their evolution patterns over time is very important in many applications. In this paper, we propose an evolutionary multi-branch tree clustering method for streaming text data. We build evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which considers both the likelihood of the current tree and conditional prior given the previous tree. We also introduce a constraint model to compute the conditional prior of a tree in the multi-branch setting. Experiments on real world news data demonstrate that our algorithm can better incorporate historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm.

A search task represents an atomic information need of a user in web search. Tasks consist of queries and their reformulations, and identifying tasks is important for search engines since they provide valuable information for determining user satisfaction with search results, predicting user search intent, and suggesting queries to the user. Traditional approaches to identifying tasks exploit either temporal or lexical features of queries. However, many query refinements are topical, which means that a query and its refinements may not be similar on the lexical level. Furthermore, multiple tasks in the same search session may interleave, which means we cannot simply order the searches by their timestamps and divide the session into multiple tasks. Thus, in order to identify tasks correctly, we need to be able to compare two queries at the semantic level. In this paper, we use a knowledgebase known as Probase to infer the conceptual meanings of queries, and automatically identify the topical query refinements in the tasks. Experimental results on real search log data demonstrate that Probase can indeed help estimate the topical affinity between queries, and thus enable us to merge queries that are topically related but dissimilar at the lexical level.

In this paper, we propose a novel constrained coclustering method to achieve two goals. First, we combine information-theoretic coclustering and constrained clustering to improve clustering performance. Second, we adopt both supervised and unsupervised constraints to demonstrate the effectiveness of our algorithm. The unsupervised constraints are automatically derived from existing knowledge sources, thus saving the effort and cost of using manually labeled constraints. To achieve our first goal, we develop a two-sided hidden Markov random field (HMRF) model to represent both document and word constraints. We then use an alternating expectation maximization (EM) algorithm to optimize the model. We also propose two novel methods to automatically construct and incorporate document and word constraints to support unsupervised constrained clustering: 1) automatically construct document constraints based on overlapping named entities (NE) extracted by an NE extractor; 2) automatically construct word constraints based on their semantic distance inferred from WordNet. The results of our evaluation over two benchmark data sets demonstrate the superiority of our approaches against a number of existing approaches.

Taxonomies, especially the ones in specific domains, are becoming indispensable to a growing number of applications. State-of-the-art approaches assume there exists a text corpus to accurately characterize the domain of interest, and that a taxonomy can be derived from the text corpus using information extraction techniques. In reality, neither assumption is valid, especially for highly focused or fast-changing domains. In this paper, we study a challenging problem: Deriving a taxonomy from a set of keyword phrases. A solution can benefit many real life applications because i) keywords give users the flexibility and ease to characterize a specific domain; and ii) in many applications, such as online advertisements, the domain of interest is already represented by a set of keywords. However, it is impossible to create a taxonomy out of a keyword set itself. We argue that additional knowledge and contexts are needed. To this end, we first use a general purpose knowledgebase and keyword search to supply the required knowledge and context. Then we develop a Bayesian approach to build a hierarchical taxonomy for a given set of keywords. We reduce the complexity of previous hierarchical clustering approaches from O(n2 log n) to O(n log n), so that we can derive a domain specific taxonomy from one million keyword phrases in less than an hour. Finally, we conduct comprehensive large scale experiments to show the effectiveness and efficiency of our approach. A real life example of building an insurance-related query taxonomy illustrates the usefulness of our approach for specific domains.

We are building an interactive visual text analysis tool that aids users in analyzing large collections of text. Unlike existing work in visual text analytics, which focuses either on developing sophisticated text analytic techniques or inventing novel text visualization metaphors, ours tightly integrates state-of-the-art text analytics with interactive visualization to maximize the value of both. In this article, we present our work from two aspects. We first introduce an enhanced, LDA-based topic analysis technique that automatically derives a set of topics to summarize a collection of documents and their content evolution over time. To help users understand the complex summarization results produced by our topic analysis technique, we then present the design and development of a time-based visualization of the results. Furthermore, we provide users with a set of rich interaction tools that help them further interpret the visualized results in context and examine the text collection from multiple perspectives. As a result, our work offers three unique contributions. First, we present an enhanced topic modeling technique to provide users with a time-sensitive and more meaningful text summary. Second, we develop an effective visual metaphor to transform abstract and often complex text summarization results into a comprehensible visual representation. Third, we offer users flexible visual interaction tools as alternatives to compensate for the deficiencies of current text summarization techniques. We have applied our work to a number of text corpora and our evaluation shows promise, especially in support of complex text analyses.

Much research has been devoted to topic detection from text, but one major challenge has not been addressed: revealing the rich relationships that exist among the detected topics. Finding such relationships is important since many applications are interested in how topics come into being, how they develop, grow, disintegrate, and finally disappear. In this paper, we present a novel method that reveals the connections between topics discovered from the text data. Specifically, our method focuses on how one topic splits into multiple topics, and how multiple topics merge into one topic. We adopt the hierarchical Dirichlet process (HDP) model, and propose an incremental Gibbs sampling algorithm to incrementally derive and refine the labels of clusters. We then characterize the splitting and merging patterns among clusters based on how labels change. We propose a global analysis process that focuses on cluster splitting and merging, and a finer granularity analysis process that helps users to better understand the content of the clusters and the evolution patterns. We also develop a visualization process to present the results.

In this paper, we present a homotopy regularization algorithm for boosting. We introduce a regularization term with adaptive weight into the boosting framework and compose a homotopy objective function. Optimization of this objective approximately composes a solution path for the regularized boosting. Following this path, we can find suitable solution efficiently using early stopping. Experiments show that this adaptive regularization method gives a more efficient parameter selection strategy than regularized boosting and semi supervised boosting algorithms, and significantly improves the performances of traditional AdaBoost and related methods.

In this paper, we present a novel exploratory visual analytic system called TIARA (Text Insight via Automated Responsive Analytics), which combines text analytics and interactive visualization to help users explore and analyze large collections of text. Given a collection of documents, TIARA first uses topic analysis techniques to summarize the documents into a set of topics, each of which is represented by a set of keywords. In addition to extracting topics, TIARA derives time-sensitive keywords to depict the content evolution of each topic over time. To help users understand the topic-based summarization results, TIARA employs several interactive text visualization techniques to explain the summarization results and seamlessly link such results to the original text. We have applied TIARA to several real-world applications, including email summarization and patient record analysis. To measure the effectiveness of TIARA, we have conducted several experiments. Our experimental results and initial user feedback suggest that TIARA is effective in aiding users in their exploratory text analytic tasks.

Mining cluster evolution from multiple correlated time-varying text corpora is important in exploratory text analytics. In this paper, we propose an approach called evolutionary hierarchical Dirichlet processes (EvoHDP) to discover interesting cluster evolution patterns from such text data. We formulate the EvoHDP as a series of hierarchical Dirichlet processes~(HDP) by adding time dependencies to the adjacent epochs, and propose a cascaded Gibbs sampling scheme to infer the model. This approach can discover different evolving patterns of clusters, including emergence, disappearance, evolution within a corpus and across different corpora. Experiments over synthetic and real-world multiple correlated time-varying data sets illustrate the effectiveness of EvoHDP on discovering cluster evolution patterns.

Topic-based text summaries promise to help average users quickly understand a text collection and derive insights. Recent research has shown that the Latent Dirichlet Allocation (LDA) model is one of the most effective approaches to topic analysis. However, the LDA-based results may not be ideal for human understanding and consumption. In this paper, we present several topic and keyword re-ranking approaches that can help users better understand and consume the LDA-derived topics in their text analysis. Our methods process the LDA output based on a set of criteria that model a user's information needs. Our evaluation demonstrates the usefulness of the methods in summarizing several large-scale, real world data sets.

This paper presents an active learning strategy for boosting. In this strategy, we construct a novel objective function to unify semi-supervised learning and active learning boosting. Minimization of this objective is achieved through alternating optimization with respect to the classifier ensemble and the queried data set iteratively. Previous semi-supervised learning or active learning methods based on boosting can be viewed as special cases under this framework. More important, we derive an efficient active learning algorithm under this framework, based on a novel query mechanism called query by incremental committee. It does not only save considerable computational cost, but also outperforms conventional active learning methods based on boosting. We report the experimental results on both boosting benchmarks and real-world database, which show the efficiency of our algorithm and verify our theoretical analysis.

Ranking aggregation is important in data mining and information retrieval. In this paper, we proposed a semi-supervised ranking aggregation method, in which the order of several item pairs are labeled as side information. The core idea is to learn a ranking function based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data are consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experiment results show our method work well.

Multi-label learning refers to the problems where an instance can be assigned to more than one category. In this paper, we present a novel Semi-supervised algorithm for Multi-label learning by solving a Sylvester Equation (SMSE). Two graphs are first constructed on instance level and category level respectively. For instance level, a graph is defined based on both labeled and unlabeled instances, where each node represents one instance and each edge weight reflects the similarity between corresponding pairwise instances. Similarly, for category level, a graph is also built based on all the categories, where each node represents one category and each edge weight reflects the similarity between corresponding pairwise categories. A regularization framework combining two regularization terms for the two graphs is suggested. The regularization term for instance graph measures the smoothness of the labels of instances, and the regularization term for category graph measures the smoothness of the labels of categories. We show that the labels of unlabeled data finally can be obtained by solving a Sylvester Equation. Experiments on RCV1 data set show that SMSE can make full use of the unlabeled data information as well as the correlations among categories and achieve good performance. In addition, we give a SMSE's extended application on collaborative filtering.

This paper proposes a semi-supervised distance metric learning algorithm for the ranking problem. Instead of giving the computer what are the important factors that affect the final rank value, we only give several most certainly ranked points which implicitly contain the knowledge of the ranking factors. Then the computer can automatically use the most certain points and plenty of unlabeded data to learn an informative metric for ranking. This metric not only can help to regress an order in the observed data, but also can be used to retrieve the data by querying new test points. Moreover, the lower-rank distance metric can be used to visualize high-dimensional data. We also present an application to the housing potential estimation problem. It is shown that the algorithm is efficient to help consultants to refine their consulting work.