Graph Neural Networks (GNNs) demonstrate promising performance in data mining yet exhibit inherent vulnerabilities to adversarial attacks. Even imperceptible perturbations degrade model performance, seriously hindering the application of GNNs in reality. In recent years, adversarial defense methods based on model architecture have gained attention for their effectiveness. However, they exhibit limited effectiveness against emerging black-box influence maximization attacks (IMAs), which aim to maximize the spread of feature perturbations through a group of influential nodes. This may leave a potential risk in real-world applications. To address this issue, we propose a Graph Adversarial Defense method based on the Hilbert-Schmidt Independence Criterion (HSIC-GAD). Specifically, the proposed method leverages hidden representations to capture the dependence between preprocessed node features and label information. On this basis, we design a regularizer that simultaneously preserves the most relevant information for downstream tasks while filtering out adversarial perturbations from the input data. A simple theoretical analysis shows that the HSIC regularizer can reduce the sensitivity of the model to adversarial inputs. Additionally, it exhibits strong universality, consistently enhancing the adversarial robustness of diverse models. Extensive experiments on real-world datasets demonstrate that HSIC-GAD outperforms state-of-the-art defense methods against IMAs.

Rationalization, a data-centric framework, aims to build self-explanatory models to explain the prediction outcome by generating a subset of human-intelligible pieces of the input data. It involves a cooperative game model where a generator generates the most human-intelligible parts of the input (i.e., rationales), followed by a predictor that makes predictions based on these generated rationales. Conventional rationalization methods typically impose constraints via regularization terms to calibrate or penalize undesired generation. However, these methods are suffering from a problem called mode collapse, in which the predictor produces correct predictions yet the generator consistently outputs rationales with collapsed patterns. Moreover, existing studies are typically designed separately for specific collapsed patterns, lacking a unified consideration. In this paper, we systematically revisit cooperative rationalization from a novel game-theoretic perspective and identify the fundamental cause of this problem: the generator no longer tends to explore new strategies to uncover informative rationales, ultimately leading the system to converge to a suboptimal game equilibrium (correct predictionsv.scollapsed rationales). To solve this problem, we then propose a novel approach, Game-theoreticPolicyOptimization orientedRATionalization (PoRat), which progressively introduces policy interventions to address the game equilibrium in the cooperative game process, thereby guiding the model toward a more optimal solution state. We theoretically analyse the cause of such a suboptimal equilibrium and prove the feasibility of the proposed method. Furthermore, we validate our method on nine widely used real-world datasets and two synthetic settings, wherePoRatachieves up to 8.1% performance improvements over existing state-of-the-art methods. Code and data are available atanonymous.4open.science/r/Rationalization-PORAT-ECE9.

Multi-criteria (MC) ratings as auxiliary supervisory signals can improve the prediction accuracy of recommender systems. The existing MC methods learn the representations of users and items in Euclidean space to estimate the interaction probabilities. However, this modeling paradigm ignores two important aspects. Firstly, when embedding power-law distribution data and personalized MC preferences in Euclidean space, the model may produce suboptimal solutions due to the distortion of the hierarchical structure. Secondly, the inevitable noise in MC ratings may hinder the recommendation quality of the model. To address the above issues, we propose a novel framework called Hyperbolic Multi-Criteria Recommendation (HMCR), which aims to mine users' MC behavioral features on hyperbolic manifolds and mitigate the noise interference through knowledge transfer among the criteria. Specifically, we map the representations on each criterion view to a hyperbolic space with adjustable curvature based on the Lorentz model, which is used to capture the hierarchical structure of collective user behavior. The MC preferences of individual users are fused by calculating the hyperbolic attention among each criterion and the overall rating. Moreover, we design a self-supervised contrastive loss to suppress the negative impact of noise interactions on the model. The experimental results on four real-world datasets show that the HMCR significantly outperforms the existing baselines.

Dynamic graph representation learning aims to capture the evolution of graph structures and obtain accurate node embeddings, a crucial task in graph machine learning. The Hawkes point process, a mathematical framework effective for modeling the influence of historical events on future occurrences, has been validated as a powerful tool for capturing the dynamics of graph evolution in dynamic graph representation learning. However, existing dynamic graph representation learning methods based on the Hawkes point process primarily model excitation at the individual node level, failing to adequately account for structural influences during graph evolution. This limitation restricts their ability to comprehensively capture network evolution patterns. To address this limitation, we propose a Hawkes Point Process-enhanced Dynamic Graph Neural Network (HP-DGNN) model. This model leverages the Hawkes point process to model both individual node histories and structural histories, capturing their respective influences on future node interactions. By integrating individual and structural influences in computing Hawkes conditional intensity, the model comprehensively captures the impacts of both layers on future node interactions. We evaluate our proposed model on two downstream tasks of dynamic graph representation learning: dynamic link prediction and future node degree prediction. Compared to 12 state-of-the-art methods, our model consistently demonstrates superior performance, underscoring its effectiveness in capturing the complexities of graph evolution.

Clustering is a fundamental technique widely used for exploring the inherent data structure. Many studies indicate that an appropriate feature representation can effectively improve clustering performance. However, the existing feature representation methods are based on correlation to select or extract features, which makes it hard to deal with spurious correlations. The spurious correlations mislead the correlation-based methods to consider features that have no causal relationship as being correlative, which limits the clustering performance and feature interpretability. To tackle this issue, inspired by causal learning, we propose a new joint optimization Clustering Method with Causal Feature Embedding (CM-CaFE) , which utilizes the causality of features to learn more discriminative representation for clustering. Specifically, to eliminate spurious correlations among features, we first employ any state-of-the-art Markov blanket learning method to learn an undirected causal graph. Next, we extract the maximal fully connected causal subgraphs from the learned undirected causal graph and propose an approach to merge them to generate the causal matrix. Based on the causal matrix, we present an objective function that consists of a clustering loss term and a causal matrix fitting term to learn a causal transformation matrix. The causal transformation matrix is utilized to map the original data into a new space for clustering. Finally, we comprehensively compare the proposed method with some state-of-the-art clustering approaches on several datasets to demonstrate the effectiveness and interpretability of the proposed method.

Feature weighting aims to assign different weights to features based on their importance in machine learning tasks. In clustering tasks, the existing methods learn feature importance based on the clustering results derived from the collaborative contribution of all features, which overlooks the independent effect of each feature. In fact, there are underlying causal relationships between features and the clustering results, and the features with high causal effects are always more crucial for clustering. Therefore, we propose an enhancedFeatureWeighting method viaCausalEffect forClustering, calculating the causal effect of each feature on the clustering results for obtaining the independent contribution of each feature. Specifically, we start by identifying the causal relationships among the features and utilizing the causal relationships to generate a reasonable treatment group. Next, we compare the changes in the data distribution between the treatment and control groups to determine the causal effect of each feature. Finally, the causal effects of features are used for enhancing the clustering-driven weight learning. Moreover, we present a theory of relative order consistency in causal effect. Experimental results demonstrate that utilizing causal effect in weight learning facilitates efficient convergence and achieves superior accuracy compared to state-of-the-art clustering algorithms.

Asynchronous pipeline model parallelism with a “1F1B” (one forward, one backward) schedule generates little bubble overhead and always provides quite a high throughput. However, the “1F1B” schedule inevitably leads to weight inconsistency and weight staleness issues due to the cross-training of different mini-batches across GPUs. To simultaneously address these two problems, in this paper, we propose an optimizer-dependent weight prediction strategy (a.k.a PipeOptim) for asynchronous pipeline training. The key insight of our proposal is that we employ a weight prediction strategy in the forward pass to approximately ensure that each mini-batch uses consistent and staleness-free weights to compute the forward pass of the “1F1B” schedule. To be concrete, we first construct the weight prediction scheme based on the update rule of the used optimizer when training the deep neural network models. Then throughout the “1F1B” pipeline training, each mini-batch is mandated to execute weight prediction, subsequently employing the predicted weights to perform the forward pass. As a result, PipeOptim 1) inherits the advantage of the “1F1B” schedule and generates high throughput, and 2) can ensure effective parameter learning regardless of the type of the used optimizer. We conducted extensive experimental evaluations using nine different deep-learning models to verify the effectiveness of our proposal. The experiment results demonstrate that PipeOptim outperforms the other five popular pipeline approaches including GPipe, PipeDream, PipeDream-2BW, SpecTrain, and XPipe.

As a promising strategy to achieve generalizable graph learning tasks, graph invariant learning emphasizes identifying invariant subgraphs for stable predictions on biased unknown distribution by selecting the important edges/nodes based on their contributions to the predictive tasks (i.e., subgraph predictivity). However, the existing approaches solely relying on subgraph predictivity face a challenge: the learned invariant subgraph often contains numerous spurious nodes and shows poor connectivity, undermining the generalization power of Graph Neural Networks (GNNs). To tackle this issue, we propose a summary graph-induced Invariant Learning (SIL) model that innovatively adopts a summary graph to leverage both the subgraph connectivity and predictivity for learning strong connected and accurate invariant subgraphs. Specifically, SIL first learns a summary graph containing multiple strongly connected supernodes while maintaining structure consistency with the original graph. Second, the learned summary graph is disentangled into an invariant supernode and spurious counterparts to eliminate the interference of highly predictive edges and nodes. Finally, SIL identifies a potential invariant subgraph from the invariant supernode to accomplish generalization tasks. Additionally, we provide a theoretical analysis of the summary graph learning mechanism, guaranteeing that the learned summary graph is consistent with the original graph. Experimental results validate the effectiveness of the SIL model.

Attributed Question Answering (AQA) aims to provide both a trustworthy answer and a reliable attribution report for a given question. Retrieval is a widely adopted approach, including two general paradigms: Retrieval-Then-Read (RTR) and post-hoc retrieval. Recently, Large Language Models (LLMs) have shown remarkable proficiency, prompting growing interest in AQA among researchers. However, RTR-based AQA often suffers from irrelevant knowledge and rapidly changing information, even when LLMs are adopted, while post-hoc retrievalbased AQA struggles with comprehending long-form answers with complex logic, and precisely identifying the content needing revision and preserving the original intent. To tackle these problems, this paper proposes an Atomic fact decompositionbased Retrieval and Editing (ARE) framework, which decomposes the generated long-form answers into molecular clauses and atomic facts by the instruction-tuned LLMs. Notably, the instruction-tuned LLMs are fine-tuned using a well-constructed dataset, generated from large scale Knowledge Graphs (KGs). This process involves extracting one-hop neighbors from a given set of entities and transforming the result into coherent long-form text. Subsequently, ARE leverages a search engine to retrieve evidences related to atomic facts, inputting these evidences into an LLM-based verifier to determine whether the facts require expansion for re-retrieval or editing. Furthermore, the edited facts are backtracked into the original answer, with evidence aggregated based on the relationship between molecular clauses and atomic facts. Extensive evaluations demonstrate the superior performance of our proposed method over the state-of-the-arts on several datasets, with an additionally proposed new metricAttrpfor evaluating the precision of evidence attribution.

Recommender systems are designed to assist users in discovering interesting items and bringing profits to online platforms. The existing works primarily explore the correlation between historical feedback and model predictions through the data-driven paradigm based on a single user-item rating matrix (i.e., overall rating). However, this single-criterion methods ignore the users’ multi-criteria (MC) behavioral characteristics. For example, a hotel system allows users to rate from multiple dimensions, such as environment and location (i.e., MC ratings). Moreover, selection bias is pervasive in user behavior data. Traditional data-driven methods may induce spurious association and amplified biases. To address the above challenges, we propose a debiasing framework called Multi-Criteria Causal Recommendation (MCCR), which encapsulates users’ diverse MC preferences and employs causal inference to construct novel training and inference strategies. Specifically, we first represent the causal relationships among variables in MC scenarios through the structural causal model. Then, we mitigate the negative impact of selection bias through the back-door adjustment. Next, a graph representation learning framework suitable for MC ratings is developed, which is used to extract higher-order information and infer the heterogeneity of users’ preferences with different criteria. Experimental results on six real datasets demonstrate that the MCCR significantly outperforms the existing baselines.

Improving the generalization performance of classifiers from data pre-processing perspective has recently received considerable attention in the machine learning community. Although many methods have been proposed in the past decades, most of them lack theoretical foundations and cannot guarantee better generalization performance of classifiers on processed datasets. To overcome this flaw, in this paper, we propose a method, which is supported by Bayesian decision theory and percolation theory, to improve generalization performance by removing Bayesian confusing samples (abbr. BCS). Specifically, for a training set, we define the samples that misclassified by the Bayesian optimal classifier as BCS and prove that a classifier trained on the training set after removing BCS can obtain better generalization performance. To find out BCS, we indicate that BCS can be identified according to the size of global homogeneous cluster, a set of samples with the same labels, based on percolation theory. Based on these analysis, we propose a method to construct global homogeneous clusters and remove BCS from the training set. Extensive experiments show that the proposed method is effective for a number of classical and state-of-the-art classifiers.

Inferring causal structures from experimentation is a challenging task in many fields. Most causal structure learning algorithms with unknown interventions are proposed to discover causal relationships over an identical variable set. However, often due to privacy, ethical, financial, and practical concerns, the variable sets observed by multiple sources or domains are not entirely identical. While a few algorithms are proposed to handle the partially overlapping variable sets, they focus on the case of known intervention targets. Therefore, to be close to the real-world environment, we consider discovering causal relationships over overlapping variable sets under the unknown intervention setting and exploring a scenario where a problem is studied across multiple domains. Here, we propose an algorithm for discovering the causal relationships over the integrated set of variables from unknown interventions, mainly handling the entangled inconsistencies caused by the incomplete observation of variables and unknown intervention targets. Specifically, we first distinguish two types of inconsistencies and then deal with respectively them by presenting some lemmas. Finally, we construct a fusion rule to combine learned structures of multiple domains, obtaining the final structures over the integrated set of variables. Theoretical analysis and experimental results on synthetic, benchmark, and real-world datasets have verified the effectiveness of the proposed algorithm.

Causal structure learning (CSL) from observational data is a crucial objective in various machine learning applications. Recent advances in CSL have focused on local-to-global learning, which offers improved efficiency and accuracy. The local-to-global CSL algorithms first learn the local skeleton of each variable in a dataset, then construct the global skeleton by combining these local skeletons, and finally orient edges to infer causality. However, data quality issues such as noise and small samples often result in the presence of problematicasymmetric edgesduring global skeleton construction, hindering the creation of a high-quality global skeleton. To address this challenge, we propose a novel local-to-global CSL algorithm with a progressive enhancement strategy and make the following novel contributions: 1) To construct an accurate global skeleton, we design a novel strategy to iteratively correctasymmetric edgesand progressively improve the accuracy of the global skeleton. 2) Based on the learned accurate global skeleton, we design an integrated global skeleton orientation strategy to infer the correct directions of edges for obtaining an accurate and reliable causal structure. Extensive experiments demonstrate that our method achieves better performance than the existing CSL methods.

Graph Neural Networks (GNNs) have attracted significant research interest in various graph data modeling tasks. To advance trustworthy, reliable, and safe Artificial Intelligence (AI) systems for practical applications, adversarial robustness learning on GNNs has drawn widespread attention among researchers. Numerous attack methods, including white-box attacks, gray-box attacks, and black-box attacks, have been proposed, but black-box attacks are widely considered to be the most challenging and practical in real-world applications. In this paper, we focus on the challenging and realistic black-box attack scenario on GNNs, where the attacker has no information about the structure and parameters of the target model. We first theoretically demonstrate that the loss changes of the GNNs are related to the node voting matrix, which is subject to the graph topology information and is independent to the structures of GNNs. Then, we propose a novel black-box attack strategy for GNNs based on the theoretical results, i.e., node voting influence-based GNNs black-box adversarial attack, named VoteAttack. Specifically, the VoteAttack algorithm iteratively chooses a group of significant nodes based on mutual voting among nodes (the node voting matrix) and considers the voting weights among nodes. Furthermore, the VoteAttack algorithm modifies the attributes of the selected nodes to create a perturbed graph and ultimately utilizes the perturbed graph to attack GNNs. Experimental results on popular GNNs and graph datasets indicate that the proposed attack strategy outperforms baseline strategies.

Electronic health records (EHRs) contain vast medical information like diagnosis, medication, and procedures, enabling personalized drug recommendations and treatment adjustments. However, current drug recommendation methods only model patients' health conditions from EHR data, neglecting the rich relationships within the data. This paper seeks to utilize a heterogeneous information network (HIN) to represent EHR and develop a graph representation learning method for medication recommendation. However, three critical issues need to be investigated: (1) co-occurrence of diagnosis and drug for the same patient does not imply their relevance; (2) patients' directly associated information may not be sufficient to reflect their health conditions; and (3) the cold start problem exists when patients have no historical EHRs. To tackle these challenges, we develop a bi-channel heterogeneous local structural encoder to decouple and extract the diverse information in HIN. Additionally, a global information capture and fusion module, aggregating meta-paths to form a global representation, is introduced to fill the information gaps in records. A longitudinal model using rich structural information available in EHR data is proposed for drug recommendations to new patients. Experimental results on real-world EHR data demonstrate significant improvements over existing approaches.

Although deep learning has been extensively studied and achieved remarkable performance on single image super-resolution (SISR), existing convolutional neural networks (CNN) mainly focus on broader and deeper architecture design, ignoring the detailed information of the image itself and the potential relationship between the features. Recently, several attempts have been made to address the SISR with graph representation learning. However, existing GNN-based methods learning to deal with the SISR problem are limited to the information processing of the entire image or the relationship processing between different feature images of the same layer, ignoring the interdependence between the extracted features of different layers, which is not conducive to extracting deeper hierarchical features. In this paper, we propose an interlayer feature representation based graph neural network for image super-resolution (LSGNN), which consists of a layer feature graph representation learning module and a channel spatial attention module. The layer feature graph representation learning module mainly captures the interdependence between the features of different layers, which can learn more fine-grained image detail features. In addition, we also unified a channel attention module and a spatial attention module into our model, which takes into account the channel dimension information and spatial scale information, to improve the expressive ability, and achieve high quality image details. Extensive experiments and ablation studies demonstrate the superiority of the proposed model.

The majority of the classical dimensionality reduction methods can be unified into a graph-embedding-based framework. A fixed graph constructed in a high-dimensional space has been extensively employed in the graph-embedding-based dimensionality reduction methods. However, a fixed graph often cannot characterize the structure of high-dimensional data owing to the curse of dimensionality. To solve this problem, we combine graph construction and dimensionality reduction into a coherent framework. Thus, the constructed graph can be updated dynamically in dimensionality reduction. In the existing methods based on the coherent framework, graphs are usually constructed by a type of neighborhood relationship and single clustering result. This study proposes an unsupervised dimensionality reduction method guided by fusing multiple clustering results. In the proposed method, multiple clustering results are first obtained by the k-means algorithm, and then a graph is constructed using a weighted co-association matrix of fusing the clustering results to capture data distribution information. Based on the graph, we present an objective function of combining graph construction and dimensionality reduction to implement mutual guidance between them. Numerical experiments on real data sets illustrate that the proposed method achieves significant improvement over some representative and state-of-the-art unsupervised dimensionality reduction methods.

Graph matching endeavors to find corresponding nodes across two or more graphs, which plays a fundamental role in many vision and pattern matching tasks. However, existing graph matching algorithms often meet abnormal graphs with missing node features and suffer from numerous cluttered outliers in practical applications. To address these, we propose a novel deep graph matching method called Random Deep Graph Matching (RDGM). Different from the deterministic affinity inference in existing deep graph matching methods, RDGM performs message passing in a random manner during model training through randomly masking some available node features in the source or target graph, so that the affinity inference between nodes is insensitive to specific neighborhoods. In addition, a hierarchical attention graph neural network framework is devised in the node embedding process of RDGM, which can obtain more sufficient high-order structural information to reduce the impact of latent noise on affinity learning. Extensive experiments suggest that the proposed RDGM outperforms state-of-the-art graph matching methods, and demonstrates strong robustness and generalization performance.

Zero-shot node classification is a very important challenge for classical semi-supervised node classification algorithms, such as Graph Convolutional Network (GCN) which has been widely applied to node classification. In order to predict the unlabeled nodes from unseen classes, zero-shot node classification needs to transfer knowledge from seen classes to unseen classes. It is crucial to consider the relations between the classes in zero-shot node classification. However, the GCN only considers the relations between the nodes, not the relations between the classes. Therefore, the GCN can not handle the zero-shot node classification effectively. This paper proposes a Dual Bidirectional Graph Convolutional Networks (DBiGCN) that consists of dual BiGCNs from the perspective of the nodes and the classes, respectively. The BiGCN can integrate the relations between the nodes and between the classes simultaneously in an united network. In addition, to make the dual BiGCNs work collaboratively, a label consistency loss is introduced, which can achieve mutual guidance and mutual improvement between the dual BiGCNs. Finally, the experimental results on real-world graph data sets verify the effectiveness of the proposed method.

Link prediction has become a significant research problem in deep learning, and the graph-based autoencoder model is one of the most important methods to solve it. The existing graph-based autoencoder models only learn a single set of distributions, which cannot accurately represent the mixed distribution in real graph data. Meanwhile, existing learning models have been greatly restricted when the graph data has insufficient attribute information and inaccurate topology information. In this paper, we propose a novel graph embedding framework, termed multi-scale variational graph autoencoder (MSVGAE), which learns multiple sets of low-dimensional vectors of different dimensions through the graph encoder to represent the mixed probability distribution of the original graph data, and performs multiple sampling in each dimension. Furthermore, a self-supervised learning strategy (i.e., graph feature reconstruction auxiliary learning) is introduced to fully use the graph attribute information to help the graph structure learning. Experiment studies on real-world graphs demonstrate that the proposed model achieves state-of-the-art performance compared with other baseline methods in link prediction tasks. Besides, the robustness analysis shows that the proposed MSVGAE method has obvious advantages in the processes of graph data with insufficient attribute information and inaccurate topology information.

Cluster ensemble is a very important tool that aggregates several base clusterings to generate a single output clustering with improved robustness and stability. However, the quality of the final clustering is often affected by uncertainties on the generation and integration of base clusterings. In this paper, we develop an information-theoretical framework which makes an effort to obtain a final clustering with high consensus on both the original data set and the base clustering set by minimizing the two uncertainties of cluster ensemble. In this framework, we provide a weighted consensus measure based on information entropy to evaluate the quality of a clustering, the similarity between clusters and the similarity between objects. Based on the measure, we propose three weighted cluster ensemble algorithms with different ensemble strategies in the framework, including the weighted feature consensus algorithm, the weighted relabeling consensus algorithm and the weighted pairwise-similarity consensus algorithm. In the experimental analysis, we compare the proposed algorithms with other existing clustering ensemble algorithms on several data sets. The comparison results illustrate the proposed algorithms are very effective and robust.

In large-scale data classification tasks, it is becoming more and more challenging in finding a true class from a huge amount of candidate categories. Fortunately, a hierarchical structure usually exists in these massive categories. The task of utilizing this structure for effective classification is called hierarchical classification. It usually follows a top-down fashion which predicts a sample from the root node with a coarse-grained category to a leaf node with a fine-grained category. However, misclassification is inevitable if the information is insufficient or large uncertainty exists in the prediction process. In this scenario, we can design a stopping strategy to stop the sample at an internal node with a coarser category, instead of predicting a wrong leaf node. Several studies address the problem by improving performance in terms of hierarchical accuracy and informative prediction. However, all of these researches ignore an important issue: when predicting a sample at the current node, the error is inclined to occur if large uncertainty exists in the next lower level children nodes. In this paper, we integrate this uncertainty into a risk problem: when predicting a sample at a decision node, it will take precipitance risk in predicting the sample to a children node in the next lower level on one hand, and take conservative risk in stopping at the current node on the other. We address the risk problem by designing a Local Bayes Risk Minimization (LBRM) framework, which divides the prediction process into recursively deciding to stop or to go down at each decision node by balancing these two risks in a top-down fashion. Rather than setting a global loss function in the traditional Bayes risk framework, we replace it with different uncertainty in the two risks for each decision node. The uncertainty on the precipitance risk and the conservative risk are measured by information entropy on children nodes and information gain from the current node to children nodes, respectively. We propose a Weighted Tree Induced Error (WTIE) to obtain the predictions of minimum risk with different emphasis on the two risks. Experimental results on various datasets show the effectiveness of the proposed LBRM algorithm.

There is always a lack of a cluster validity function and optimization strategy to find out clusters and catch the evolution trend of cluster structures on a categorical data stream. Therefore, this paper presents an optimization model for clustering categorical data streams. In the model, a cluster validity function is proposed as the objective function to evaluate the effectiveness of the clustering model while each new input data subset is flowing. It simultaneously considers the certainty of the clustering model and the continuity with the last clustering model in the clustering process. An iterative optimization algorithm is proposed to solve an optimal solution of the objective function with some constraints. Furthermore, we strictly derive a detection index for drifting concepts from the optimization model. We propose a detection method that integrates the detection index and the optimization model to catch the evolution trend of cluster structures on a categorical data stream. The new method can effectively avoid ignoring the effect of the clustering validity on the detection result. Finally, using the experimental studies on several real data sets, we illustrate the effectiveness of the proposed algorithm in clustering categorical data streams, compared with existing data-streams clustering algorithms.

Cold-start link prediction is a term for information starved link prediction where little or no topological information is present to guide the determination of whether links to a node will form. Due to the lack of topological information, traditional topology-based link prediction methods cannot be applied to solve the cold-start link prediction problem. Therefore, an effective approach is presented through establishing connections between non-topological and topological information. In the approach, topological information is first extracted by a latent-feature representation model, then a logistic model is proposed to establish the connections between topological and non-topological information, and finally the linking possibility between cold-start users and existing users is calculated. Experiments with three types of real-world social networks Weibo, Facebook, and Twitter show that the proposed approach is more effective in solving the cold-start link prediction problem and establishing connections between topological and non-topological information.

Ordinal classification with a monotonicity constraint is a kind of classification tasks, in which the objects with better attribute values should not be assigned to a worse decision class. Several learning algorithms have been proposed to handle this kind of tasks in recent years. The rank entropy-based monotonic decision tree is very representative thanks to its better robustness and generalization. Ensemble learning is an effective strategy to significantly improve the generalization ability of machine learning systems. The objective of this work is to develop a method of fusing monotonic decision trees. In order to achieve this goal, we take two factors into account: attribute reduction and fusing principle. Through introducing variable dominance rough sets, we firstly propose an attribute reduction approach with rank-preservation for learning base classifiers, which can effectively avoid overfitting and improve classification performance. Then, we establish a fusing principe based on maximal probability through combining the base classifiers, which is used to further improve generalization ability of the learning system. The experimental analysis shows that the proposed fusing method can significantly improve classification performance of the learning system constructed by monotonic decision trees.

Many real data increase dynamically in size. This phenomenon occurs in several fields including economics, population studies, and medical research. As an effective and efficient mechanism to deal with such data, incremental technique has been proposed in the literature and attracted much attention, which stimulates the result in this paper. When a group of objects are added to a decision table, we first introduce incremental mechanisms for three representative information entropies and then develop a group incremental rough feature selection algorithm based on information entropy. When multiple objects are added to a decision table, the algorithm aims to find the new feature subset in a much shorter time. Experiments have been carried out on eight UCI data sets and the experimental results show that the algorithm is effective and efficient.