Spatio-temporal kriging is an essential research problem in sensor networks due to the sparsity of deployed sensors. While recent studies consider spatial and temporal correlations, they often overlook the sparse spatial distribution of locations and the incomplete features across locations. To tackle these problems, we propose an Anchor-based Incremental and Stratified Graph Learning Framework for Inductive Spatio-Temporal Kriging (AnchorGK). AnchorGK introduces anchor locations to enable effective data stratification for accurate kriging. Anchor locations are constructed based on feature availability, and strata are subsequently established based on the an- chor locations. This stratification serves two purposes: 1) it ensures that the spatial correlations between unknown areas (no observations) and surrounding known locations are accurately represented and dynamically updated within the graph learning framework, and 2) it facilitates the use of all available features across different strata through a novel incremental representation method. Building on the data stratification, we propose a dual-view graph learning layer that integrates information from relevant features and locations and learns distinct representations for different strata. Finally, kriging is performed based on the obtained strata representations. Experimental results on multiple benchmark datasets demonstrate that AnchorGK consistently outperforms existing state-of-the-art methods. Our codes, datasets, and related materials are given in: https://github.com/xren451/Spatial-interpolation

Analogical reasoning over Multimodal Knowledge Graphs (MMKGs) couples abductive relation induction with inductive tail completion. However, existing approaches rely on static fusion mechanisms that overlook the inherent asymmetry of modal relevance: while visual cues elucidate concrete entities, they are often noisy or irrelevant for abstract concepts, where text and graph structure provide decisive signals. Furthermore, prior methods fail to enforce consistency between induced relations and the modality patterns implied by the analogical context. To bridge this gap, we introduce RMAR, a Relation-aware Multimodal Analogical Reasoning framework with two complementary paths. An explicit path estimates modality fingerprints to score compatibility during relation induction and guide fusion during tail completion. An implicit path employs adaptive gating to blend structural, textual, and visual signals conditioned on the specific query context. To address the limitations of current benchmarks, which overrepresent concrete entities, we release MCNetAnalogy, and its companion graph, MCNetKG, a rigorous dataset enriched with abstract concepts and actions. RMAR is backbone-agnostic and works with multimodal knowledge graph embedding (MKGE) and transformer-based (MPT) pipelines. Extensive experiments demonstrate that RMAR delivers consistent gains across both embedding-based and transformer-based backbones, achieving a 29% relative improvement on MCNetAnalogy. Ablation studies confirm that RMAR's relation-aware modulation is particularly effective when modal evidence is weak or ambiguous.

Deep trajectory modeling has garnered significant attention across various applications, particularly in trajectory user linking (TUL), which aims to associate trajectories with specific users by analyzing complex mobility patterns. Despite its advancements, the lack of explainability remains a critical challenge. In this article, we propose a general Information Bottleneck framework, TUL-IB, designed to enhance the explainability of TUL models for both sequence and graph data, with tractable optimization bounds to solve the TUL-IB objective. We further demonstrate that TUL-IB can be effectively applied to two distinct types of trajectory data: (1) waypoint trajectories, for which we extend TUL-IB into a dual-view approach, TUL-DV-IB, integrating both driving behavior sequences and trajectory road graphs. To ensure temporal continuity in subsequence selection, we employ dynamic programming during post-processing; (2) staypoint trajectories, for which we adapt TUL-IB to the graph node level and apply it to global trajectory graph model, resulting in TUL-GTG-IB. This adaptation identifies key neighboring trajectories that significantly contribute to explaining the user-linking results. Experimental results on three real-world datasets demonstrate that our method outperforms existing explainable approaches, providing deeper insights into trajectory user-linking models.

Urban spatial tasks span multiple levels, ranging from area-level analysis, crime prediction, and taxi demand forecasting to POI-level tasks such as new store recommendation. Urban knowledge graphs (UrbanKGs) can enhance these tasks by integrating structured urban knowledge. However, existing studies face two main issues: most research uses task-specific UrbanKGs for corresponding single-level predictions, and public UrbanKGs contain only coarse-grained administrative areas, lacking the rich semantic and spatial relationships required for multi-level tasks. We propose a Hierarchically Structured UrbanKG Dataset (HUSK) with an intermediate functional zone layer that bridges and enriches the understanding across multiple levels, and evaluate it on three area-level and three POI-level tasks, showing accuracy improvements over single-view baselines.

Trajectory anomaly detection is critical in trajectory data mining. The objective is to identify abnormal movements of objects. Most existing trajectory anomaly detection methods focus on determining whether an entire trajectory is anomalous, lacking the ability to identify the exact anomalous sub-trajectories. Although recent research has started addressing anomalous sub-trajectories detection, these methods fail to extract the specific route pattern for the target trajectory. As a result, they struggle to identify anomalous sub-trajectories when the same sub-trajectory is regarded as normal in other routes. To overcome these limitations, we propose a Route-Enhanced Conditional Anomalous Sub-Trajectory detection model (RECAST). RECAST has two innovative components: (1) a Route Discovery Network (RDN) that extracts the normal route pattern of the given trajectory; (2) a Conditional Anomalous Sub-trajectory Detection (CASD) network that detects anomalies conditioned on the estimated route patterns. Our design enables RECAST to identify sub-trajectories as anomalous even if they are normal in other routes, as long as they are unlikely to occur in the route of the given trajectory. We evaluate the effectiveness and efficiency of RECAST using two real-world datasets. The results demonstrate that our method outperforms the state-of-the-art methods in detection accuracy with competitive runtime efficiency1.

Cardinality estimation is a fundamental functionality in database systems. Most existing cardinality estimators focus on handling predicates over numeric or categorical data. They have largely omitted an important data type, set-valued data, which frequently occur in contemporary applications such as information retrieval and recommender systems. The few existing estimators for such data either favor high-frequency elements or rely on a partial independence assumption , which limits their practical applicability. We propose ACE, an Attention-based Cardinality Estimator for estimating the cardinality of queries over set-valued data. We first design a distillation-based data encoder to condense the dataset into a compact matrix. We then design an attention-based query analyzer to capture correlations among query elements. To handle variable-sized queries, a pooling module is introduced, followed by a regression model (MLP) to generate final cardinality estimates. We evaluate ACE on three datasets with varying query element distributions, demonstrating that ACE outperforms the state-of-the-art competitors in terms of both accuracy and efficiency.

Time series prediction presents a significant challenge across various domains, such as transportation systems, environmental science, and multiple industrial sectors. Real-world time series data commonly exhibit periodic patterns and irregular sampling rates. Recent advancements in long sequence time series forecasting have made significant progress in adopting deep neural networks, particularly the Transformers, renowned for their robust representational capabilities. However, current Transformer-based models consider time steps as discrete tokens, thereby failing to account for periodicity and temporal intervals when selecting relevant time steps in the past. To address this limitation, we propose an end-to-end framework called Periormer for forecasting irregularly sampled time series. Periormer comprises three key components: (1) a novel input embedding layer that encodes the periodicity and time interval information, analogous to positional encoding in Transformers; (2) a feature-wise periodic attention mechanism that selects essential data points considering the periods and amplitudes of the periodic signals; and (3) a cross-feature periodic attention mechanism that identifies essential features relevant to the prediction. Experiments on four real-world datasets and one synthetic dataset demonstrate that Periormer reduces the mean squared error by 14.9% compared to state-of-the-art models.

Point-of-Interest (POI) search is vital for location-based services, aiding users in finding relevant locations. While general textual semantic matching and spatial keyword queries have been well-studied, personalized POI search has received less attention. Analyzing real-world POI search data reveals that user preferences for geographical distances and regions vary and are dynamic, highlighting the need for query-dependent personalization. To address this, we introduce the QPL (Query-dependent user Preference on geographical factors Learning) model. This model simultaneously addresses textual relevance and user preference learning. It features a novel textual matching module that combines traditional lexical matching with deep semantic relevance, and a user preference learning module that adapts to query-dependent preferences for distance and region. A Cross Attention Layer further captures the relationship between query and POI regions. Extensive experiments on two real-world datasets demonstrate our model's effectiveness. The source code is available at https://github.com/Shawn-hub-hit/QPL-master

Dynamic networks are data structures that represent the interactions among various entities in real-world systems, with their topology and node properties evolving over time. However, prevailing approaches typically derive node embeddings through aggregating temporal neighbor nodes of adjacent several hops, thus failing to capture the long temporal dependencies in dynamic networks. Furthermore, existing research on dynamic networks focuses on node- and edge-level tasks, lacking the support of graph-level tasks. To address the limitations of current approaches, this paper proposes TP-GNN, a novel continuous dynamic graph neural network model intended for graph classification in dynamic networks, which offers two primary advantages: (1) TP-GNN captures the long temporal dependencies via a novel message-passing method based on the information flow among the nodes, and (2) it learns the network evolution process from edge order for accurate dynamic network analytics. We evaluate the performance of TP-GNN in five datasets, including a new dataset we created from a Java software project. The results show that our method outperforms state-of-the-art approaches in graph classification with an average improvement of 4.91% in terms of$F_{1}$Score11Codes and dataset are available at https://github.com/Jie-0828/TP-GNN..

Abstract In this paper, we formulate a novel Point-of-interest (POI) recommendation task to recommend a set of new POIs for visit in a short period following recent check-ins, named short-term POI recommendation. It differs from previously studied tasks and poses new challenges, such as modeling high-order POI transitions in a short period. We present PTWLR, a personalized time-weighted latent ranking model that jointly learns short-term POI transitions and user preferences with our proposed temporal weighting scheme to capture the temporal context of transitions. We extend our model to accommodate the transition dependencies on multiple recent check-ins. In experiments on real-world datasets, our model consistently outperforms seven widely used methods by significant margins in various contexts, demonstrating its effectiveness on our task. Further analysis shows that all proposed components contribute to performance improvement.

System logs, recording critical information about system operations, serve as indispensable tools for system anomaly detection. Graph-based methods have demonstrated superior performance compared to other methods in capturing the interdependencies of log events. However, existing methods often neglect the complex substructure patterns of nodes within log graphs, making it challenging to capture the subtle alteration in event type, structure, and the location of exceptions that indicate node anomalies. To address this limitation, this paper proposes a novel framework called Substructure-aware Log Anomaly Detection at Code File Level (SLAD). It first introduces a Monte Carlo Tree Search strategy tailored specifically for log anomaly detection to discover representative substructures. Then, SLAD incorporates a substructure distillation way to enhance the efficiency of anomaly inference based on the representative substructures. After that, we introduce a soft pruning to obtain key substructure for nodes. Experimental results show SLAD outperforms all baselines. Particularly, SLAD demonstrates at least 15 times faster than substructure-based graph learning methods in anomaly inference.

This paper is the first to use contrastive learning to improve the robustness of graph representation learning for signed bipartite graphs, which are commonly found in social networks, recommender systems, and paper review platforms. Existing contrastive learning methods for signed graphs cannot capture implicit relations between nodes of the same type in signed bipartite graphs, which have two types of nodes and edges only connect nodes of different types. We propose a Signed Bipartite Graph Contrastive Learning (SBGCL) method to learn robust node representation while retaining the implicit relations between nodes of the same type. SBGCL augments a signed bipartite graph with a novel two-level graph augmentation method. At the top level, we maintain two perspectives of the signed bipartite graph, one presents the original interactions between nodes of different types, and the other presents the implicit relations between nodes of the same type. At the bottom level, we employ stochastic perturbation strategies to create two perturbed graphs in each perspective. Then, we construct positive and negative samples from the perturbed graphs and design a multi-perspective contrastive loss to unify the node presentations learned from the two perspectives. Results show proposed model is effective over state-of-the-art methods on real-world datasets.

Signed graphs model complex relations using both positive and negative edges. Signed graph neural networks (SGNN) are powerful tools to analyze signed graphs. We address the vulnerability of SGNN to potential edge noise in the input graph. Our goal is to strengthen existing SGNN allowing them to withstand edge noises by extracting robust representations for signed graphs. First, we analyze the expressiveness of SGNN using an extended Weisfeiler-Lehman (WL) graph isomorphism test and identify the limitations to SGNN over triangles that are unbalanced. Then, we design some structure-based regularizers to be used in conjunction with an SGNN that highlight intrinsic properties of a signed graph. The tools and insights above allow us to propose a novel framework, Robust Signed Graph Neural Network (RSGNN), which adopts a dual architecture that simultaneously denoises the graph while learning node representations. We validate the performance of our model empirically on four real-world signed graph datasets, i.e., Bitcoin_OTC, Bitcoin_Alpha, Epinion and Slashdot, RSGNN can clearly improve the robustness of popular SGNN models. When the signed graphs are affected by random noise, our method outperforms baselines by up to 9.35% Binary-F1 for link sign prediction. Our implementation is available in PyTorch1.

Missing data imputation for location-based sensor data has attracted much attention in recent years. The state-of-the-art imputation methods based on graph neural networks have a priori assumption that the spatial correlations between sensor locations are static. However, real-world data sets often exhibit dynamic spatial correlations. This paper proposes a novel approach to capturing the dynamics of spatial correlations between geographical locations as a composition of the constant, long-term trends and periodic patterns. To this end, we design a new method called Dynamic Adjacency Matrix Representation (DAMR) that extracts various dynamic patterns of spatial correlations and represents them as adjacency matrices. The adjacency matrices are then aggregated and fed into a well-designed graph representation learning layer for predicting the missing values. Through extensive experiments on six real-world data sets, we demonstrate that DAMR reduces the MAE by up to 19.4% compared with the state-of-the-art methods for the missing value imputation task

Spatial objects often come with textual information, such as Points of Interest (POIs) with their descriptions, which are referred to as geo-textual data. To retrieve such data, spatial keyword queries that take into account both spatial proximity and textual relevance have been extensively studied. Existing indexes designed for spatial keyword queries are mostly built based on the geo-textual data without considering the distribution of queries already received. However, previous studies have shown that utilizing the known query distribution can improve the index structure for future query processing. In this paper, we propose WISK, a learned index for spatial keyword queries, which self-adapts for optimizing querying costs given a query workload. One key challenge is how to utilize both structured spatial attributes and unstructured textual information during learning the index. We first divide the data objects into partitions, aiming to minimize the processing costs of the given query workload. We prove the NP-hardness of the partitioning problem and propose a machine learning model to find the optimal partitions. Then, to achieve more pruning power, we build a hierarchical structure based on the generated partitions in a bottom-up manner with a reinforcement learning-based approach. We conduct extensive experiments on real-world datasets and query workloads with various distributions, and the results show that WISK outperforms all competitors, achieving up to 8× speedup in querying time with comparable storage overhead.

The wide spread of rumors inflicts damages on social media platforms. Detecting rumors has become an emerging problem concerning the public and government. A crucial problem for rumors detection on social media is the lack of reliably pre-annotated dataset to train classification models. To solve this problem, we propose an unsupervised model that detects rumors by measuring how well the tweets follow the normal patterns. However, the problem is challenging in how to automatically discover the normal patterns of tweets. To tackle the challenge, we first propose a novel tree variational autoencoder model that reconstructs the sentiment labels along the propagation tree of a factual tweet. Then we propose a cross-alignment method to align the multiple modalities, i.e., tree structure and propagation features, and output the final prediction results. We conduct extensive experiments on a real-world dataset collected from Weibo. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised methods and adapts better to the concept drift than state-of-the-art supervised methods.

Representing words as embeddings has been proven to be successful in improving the performance in many natural language processing tasks. Different from the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits of pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enriched ensemble method to combine information from both knowledge graphs and pre-trained word embeddings. Specifically, we propose an attention network to retrofit the semantic information in the lexical knowledge graph into the pre-trained word embeddings. In addition, we further extend our method to contextual word embeddings and multi-sense embeddings. Extensive experiments demonstrate that the proposed word embeddings outperform the state-of-the-art models in word analogy, word similarity and several downstream tasks. The proposed word sense embeddings outperform the state-of-the-art models in word similarity and word sense induction tasks.

Graph embedding, which aims to learn low-dimensional node representations to preserve original graph structures, has attracted extensive research interests. However, most existing graph embedding models represent nodes in Euclidean spaces, which cannot effectively preserve complex patterns, e.g., hierarchical structures. Very recently, several hyperbolic embedding models have been proposed to preserve the hierarchical information in negative curvature spaces. Nevertheless, existing hyperbolic models fail to model the asymmetric proximity between nodes. To address this, we investigate a new asymmetric hyperbolic network representation problem, which targets at jointly preserving the hierarchical structures and asymmetric proximity for general directed graphs. We solve this problem by proposing a novelRotatedLorentzianEmbedding (ROLE) model, which yields two main benefits. First, our model can effectively capture both implicit and explicit hierarchical structures that come from the network topology and category information of nodes, respectively. Second, it can model the asymmetric proximity using rotation transformations. Specifically, we represent each node with a Lorentzian embedding vector, and learn two rotation matrices to reflect the direction of edges. We conduct extensive experiments on four real-world directed graph datasets. Empirical results demonstrate that the proposed approach consistently outperforms various state-of-the-art embedding models. In particular, ROLE achieves HR@1 scores up to 19.8% higher and NDCG@5 scores up to 11.3% higher than the best baselines on the task of node recommendation.

With the proliferation of online social networks, a great deal of online user action data has been generated. Such data has enabled the study of information diffusion prediction, which is a fundamental problem for understanding the propagation of information on social media platforms. In diffusion prediction models, there are two standard components, i.e., a social graph and information diffusion cascades. We observe that both components exhibit latent hierarchical structures. However, most existing models are designed based on euclidean spaces, and hence cannot effectively capture complex patterns, especially hierarchical structures. Therefore, we investigate a novel research problem to learn hyperbolic representations for information diffusion prediction. To reflect the different characteristics of social graphs and diffusion cascades, we encode them into two latent hyperbolic spaces with different trainable curvatures. In addition, to model influence dependencies, we propose a co-attention mechanism to capture the processes of diffusion cascades using positional embeddings. Given a set of activated seed users, we jointly exploit diffusion cascades and social links to predict which users will be influenced. We conduct extensive experiments on four real-world datasets. Empirical results demonstrate that the proposed H-Diffu model significantly outperforms several state-of-the-art diffusion prediction frameworks.

The cover tree (CT) and its improved version are hierarchical data structures that simplified navigating nets while maintaining good runtime guarantees. They can perform nearest neighbor search in logarithmic time and provide efficient computation in practice. In this article, we revisit cover trees for nearest neighbor search, and propose a more competitive method. The central idea of our method is to fully exploit the unused distance and direction information. More specially, our method introduces three novel concepts/techniques: (i) range list, (ii) quadrant information, and (iii) vectorial angle cosine. These techniques are seamlessly integrated into our suggested data structure and search algorithms. As an extra bonus, we explore approximate nearest neighbor and$k$nearest neighbor based on the proposed techniques, and present algorithms for handling updates. Extensive experimental results, based on both real and synthetic datasets, consistently demonstrate that our method is attractive and competitive, compared against existing cover tree structures for nearest neighbor search and its variants.

Next POI recommendation intends to forecast users' immediate future movements given their current status and historical information, yielding great values for both users and service providers. However, this problem is perceptibly complex because various data trends need to be considered together. This includes the spatial locations, temporal contexts, user's preferences, etc. Most existing studies view the next POI recommendation as a sequence prediction problem while omitting the collaborative signals from other users. Instead, we propose a user-agnostic global trajectory flow map and a novel Graph Enhanced Transformer model (GETNext) to better exploit the extensive collaborative signals for a more accurate next POI prediction, and alleviate the cold start problem in the meantime. GETNext incorporates the global transition patterns, user's general preference, spatio-temporal context, and time-aware category embeddings together into a transformer model to make the prediction of user's future moves. With this design, our model outperforms the state-of-the-art methods with a large margin and also sheds light on the cold start challenges within the spatio-temporal involved recommendation problems.

Geographical topic models have been used to mine geo-tagged documents for topical region and geographical topics, and also have applications in recommendations, user mobility modeling, event detection, etc. Existing studies focus on learning effective geographical topic models while ignoring the efficiency issue. However, it is very expensive to train geographical topic models — it may take days to train a geographical topic model of a small scale on a collection of documents with millions of word tokens. In this paper, we propose the first distributed solution, called${\sf PGeoTopic}$, for training geographical topic models. The proposed solution comprises several novel technical components to increase parallelism, reduce memory requirement, and reduce communication cost. Experiments show that our approach for mining geographical topic models is scalable with both model size and data size on distributed systems.

Recently, network embedding has attracted extensive research interest. Most existing network embedding models are based on Euclidean spaces. However, Euclidean embedding models cannot effectively capture complex patterns, especially latent hierarchical structures underlying in real-world graphs. Consequently, hyperbolic representation models have been developed to preserve the hierarchical information. Nevertheless, existing hyperbolic models only capture the first-order proximity between nodes. To this end, we propose a new embedding model, named Node2LV, that learns the hyperbolic representations of nodes using squared Lorentzian distances. This yields three advantages. First, our model can effectively capture hierarchical structures that come from the network topology. Second, compared with the conventional hyperbolic embedding methods that use computationally expensive Riemannian gradients, it can be optimized in a more efficient way. Lastly, different from existing hyperbolic embedding models, Node2LV captures higher-order proximities. Specifically, we represent each node with two hyperbolic embeddings, and make the embeddings of related nodes close to each other. To preserve higher-order node proximity, we use a random walk strategy to generate local neighborhood context. We conduct extensive experiments on four different types of real-world networks. Empirical results demonstrate that Node2LV significantly outperforms various graph embedding baselines.

Traffic flow forecasting is a crucial task in urban computing. The challenge arises as traffic flows often exhibit intrinsic and latent spatio-temporal correlations that cannot be identified by extracting the spatial and temporal patterns of traffic data separately. We argue that such correlations are universal and play a pivotal role in traffic flow. We put forward spacetime interval learning as a paradigm to explicitly capture these correlations through a unified analysis of both spatial and temporal features. Unlike the state-of-the-art methods, which are restricted to a particular road network, we model the universal spatio-temporal correlations that are transferable from cities to cities. To this end, we propose a new spacetime interval learning framework that constructs a local-spacetime context of a traffic sensor comprising the data from its neighbors within close time points. Based on this idea, we introduce local spacetime neural network (STNN), which employs novel spacetime convolution and attention mechanism to learn the universal spatio-temporal correlations. The proposed STNN captures local traffic patterns, which does not depend on a specific network structure. As a result, a trained STNN model can be applied on any unseen traffic networks. We evaluate the proposed STNN on two public real-world traffic datasets and a simulated dataset on dynamic networks. The experiment results show that STNN not only improves prediction accuracy by 15% over state-of-the-art methods, but is also effective in handling the case when the traffic network undergoes dynamic changes as well as the superior generalization capability.

Recommender system (RS) has become a crucial module in most web-scale applications. Recently, most RSs are in the waterfall form based on the cloud-to-edge framework, where recommended results are transmitted to edge (e.g., user mobile) by computing in advance in the cloud server. Despite effectiveness, network bandwidth and latency between cloud server and edge may cause the delay for system feedback and user perception. Hence, real-time computing on edge could help capture user preferences more preciously and thus make more satisfactory recommendations. Our work, to our best knowledge, is the first attempt to design and implement the novel Recommender System on Edge (EdgeRec), which achieves Real-time User Perception and Real-time System Feedback. Moreover, we propose Heterogeneous User Behavior Sequence Modeling and Context-aware Reranking with Behavior Attention Networks to capture user's diverse interests and adjust recommendation results accordingly. Experimental results on both the offline evaluation and online performance in Taobao home-page feeds demonstrate the effectiveness of EdgeRec.

Detecting anomalous trajectory has become an important and fundamental concern in many real-world applications. However, most of the existing studies 1) cannot handle the complexity and variety of trajectory data and 2) do not support efficient anomaly detection in an online manner. To this end, we propose a novel model, namely Gaussian Mixture Variational Sequence AutoEncoder (GM-VSAE), to tackle these challenges. Our GM-VSAE model is able to (1) capture complex sequential information enclosed in trajectories, (2) discover different types of normal routes from trajectories and represent them in a continuous latent space, and (3) support efficient online detection via trajectory generation. Our experiments on two real-world datasets demonstrate that GM-VSAE is more effective than the state-of-the-art baselines and is efficient for online anomalous trajectory detection.

As the first and foremost step of typical automatic log analysis, log parsing has attracted a lot of interest. Most of existing studies treat log messages as pure strings and rely on string matching or string distance. In NLP, word2vec has shown very efficient and effective in representing words with low dimensional vectors. Inspired by this, in this paper we propose a novel method, called LPV (Log Parser based on Vectorization), for both offline and online log parsing. The central idea of our method in offline log parsing is to first convert log messages into vectors, and measure the similarity between two log messages by the distance between two vectors, then log messages can be clustered via clustering the vectors, and log templates can be extracted from the resulting clusters. For online log parsing, we also assign log templates with some kind of average vectors, so that the similarity between an incoming log message and each log template can also be measured by the distance between two vectors. We have conducted extensive experiments based on three widely used log datasets, and the results demonstrate that our proposed method LPV can achieve a competitive performance, compared against state-of-the-art log parsing methods.

Representing words as embeddings in a continuous vector space has been proven to be successful in improving the performance in many natural language processing (NLP) tasks. Beyond the traditional methods that learn the embeddings from large text corpora, ensemble methods have been proposed to leverage the merits from pre-trained word embeddings as well as external semantic sources. In this paper, we propose a knowledge-enhanced ensemble method to combine both knowledge graphs and pre-trained word embedding models. Specifically, we interpret relations in knowledge graphs as linear translation from one word to another. We also propose a novel weighting scheme to further distinguish edges in the knowledge graph with same type of relation. Extensive experiments demonstrate that our proposed method is up to 20% times better than state-of-the-art in word analogy task and up to 16% times better than state-of-the-art in word similarity task.

Textual reviews, which are readily available on many e-commerce and review websites such as Amazon and Yelp, serve as an invaluable source of information for recommender systems. However, not all parts of the reviews are equally important, and the same choice of words may reflect a different meaning based on its context. In this paper, we propose a novel end-to-end Aspect-based Neural Recommender (ANR) to perform aspect-based representation learning for both users and items via an attention-based component. Furthermore, we model the multi-faceted process behind how users rate items by estimating the aspect-level user and item importance by adapting the neural co-attention mechanism. Our proposed model concurrently address several shortcomings of existing recommender systems, and a thorough experimental study on 25 benchmark datasets from Amazon and Yelp shows that ANR significantly outperforms recently proposed state-of-the-art baselines such as DeepCoNN, D-Attn and ALFM.

Trajectory similarity computation is fundamental functionality with many applications such as animal migration pattern studies and vehicle trajectory mining to identify popular routes and similar drivers. While a trajectory is a continuous curve in some spatial domain, e.g., 2D Euclidean space, trajectories are often represented by point sequences. Existing approaches that compute similarity based on point matching suffer from the problem that they treat two different point sequences differently even when the sequences represent the same trajectory. This is particularly a problem when the point sequences are non-uniform, have low sampling rates, and have noisy points. We propose the first deep learning approach to learning representations of trajectories that is robust to low data quality, thus supporting accurate and efficient trajectory similarity computation and search. Experiments show that our method is capable of higher accuracy and is at least one order of magnitude faster than the state-of-the-art methods for k-nearest trajectory search.

With the proliferation of mobile devices and location-based services, rich geo-tagged data is becoming prevalent and this offer great opportunities to understand different geographical regions (e.g., shopping areas). However, the huge number of regions with complicated spatial information are expensive for people to explore and understand. To solve this issue, we study the problem of searching similar regions given a user specified query region. The problem is challenging in both similarity definition and search efficiency. To tackle the two challenges, we propose a novel solution equipped by (1) a deep learning approach to learning the similarity that considers both object attributes and the relative locations between objects; and (2) an efficient branch and bound search algorithm for finding top-N similar regions. Moreover, we propose an approximation method to further improve the efficiency by slightly sacrificing the accuracy. Our experiments on three real world datasets demonstrate that our solution improves both the accuracy and search efficiency by a significant margin compared with the state-of-the-art methods.

Biclustering is a data mining technique that allows simultaneous clustering of two variables. A common biclustering task for categorical variables is to find ‘heavy’ biclusters, i.e., biclusters with high co-occurrence values. Although algorithms have been proposed to extract heavy biclusters, they provide little information about relative importance of each bicluster, as well as importance of the variables for each bicluster. To address these problems, there have been attempts to apply mixture models using information theory or Bayesian method. Although they are able to rank the biclusters and the variables for each bicluster, they do not target at extracting heavy biclusters. Furthermore, these models constrain the search for biclusters in such a way that every cell in the matrix must participate in some bicluster. We attempt to alleviate these limitations using dual topic models. First of all, we develop a generalized LDA topic model that extracts dual topics, i.e., topics in opposite directions – row- and column-topics. To obtain better topics, it applies mutual reinforcement, i.e., considering column-topics while constructing row-topics, and vice versa. Heavy biclusters, the high co-occurred relationship, are extracted using thresholds. We show that our model Dual Topic to Biclusters (DT2B) is effective in extracting heavy biclusters by experimenting over a simulated data, a text corpus (NIPS author-document) and a microarray gene expression data. Results show that biclusters extracted by DT2B are better.

Microblogging services like Twitter contain abundant of user generated content covering a wide range of topics. Many of the tweets can be associated to real-world entities for providing additional information for the latter. In this paper, we aim to associate tweets that are semantically related to real-world locations or Points of Interest (POIs). Tweets contain dynamic and real-time information while POIs contain relatively static information. The tweets associated with POIs provide complementary information for many applications like opinion mining and POI recommendation; the associated POIs can also be used as POI tags in Twitter. We define the research problem of annotating POIs with tweets and propose a novel supervised Bayesian Model (sBM). The model takes into account the textual, spatial features and user behaviors together with the supervised information of whether a tweet is POI-related. It is able to capture user interests in latent regions for the prediction of whether a tweet is POI-related and the association between the tweet and its most semantically related POI. On tweets and POIs collected for two cities (New York City and Singapore), we demonstrate the effectiveness of our models against baseline methods.

Huge amounts of data with both spatial and temporal information (e.g., geo-tagged tweets) are being generated, and are often used to share and spread personal updates, spontaneous ideas, and breaking news. We refer to such data as spatio-temporal documents. It is of great interest to explore topics in a collection of spatio-temporal documents.

With the increasing popularity of mobile devices and location based services, massive amount of geo-textual data (e.g., geo-tagged tweets) is being generated everyday. Compared with traditional spatial data, the textual dimension of geo-textual data greatly enriches the data. Meanwhile, the spatial dimension of geo-textual data also adds a semantically rich new aspect to textual data. The large volume, together with its rich semantics, calls for the need for data exploration. First, it has many applications to retrieve a region for exploration that satisfies user-specified conditions (e.g., the size and shape of the region) while maximizing some other conditions (e.g., the relevance to the query keywords of the objects in the region). Second, it is useful to mine and explore the topics of the geo-textual data within a (specified or retrieved) region and perhaps a timespan. This demonstration proposal presents the main ideas of our system, the RegIon Search and Exploration System (RISE), for efficiently supporting region search and exploration, and our demonstration plan.

Rich geo-textual data is available online and the data keeps increasing at a high speed. We propose two user behavior models to learn several types of user preferences from geo-textual data, and a prototype system on top of the user pre ference models for min ing and s e a r ch geo-textual data (called PreMiner) to support personalized maps. Different from existing recommender systems and data analysis systems, PreMiner highly personalizes user experience on maps and supports several applications, including user mobility & interests mining, opinion mining in regions, user recommendation, point-of-interest recommendation, and querying and subscribing on geo-textual data.

Many location based services, such as FourSquare, Yelp, TripAdvisor, Google Places, etc., allow users to compose reviews or tips on points of interest (POIs), each having a geographical coordinates. These services have accumulated a large amount of such geo-tagged review data, which allows deep analysis of user preferences in POIs. This paper studies two types of user preferences to POIs: topical-region preference and category aware topical-aspect preference. We propose a unified probabilistic model to capture these two preferences simultaneously. In addition, our model is capable of capturing the interaction of different factors, including topical aspect, sentiment, and spatial information. The model can be used in a number of applications, such as POI recommendation and user recommendation, among others. In addition, the model enables us to investigate whether people like an aspect of a POI or whether people like a topical aspect of some type of POIs (e.g., bars) in a region, which offer explanation for recommendations. Experiments on real world datasets show that the model achieves significant improvement in POI recommendation and user recommendation in comparison to the state-of-the-art methods. We also propose an efficient online recommendation algorithm based on our model, which saves up to 90% computation time.

Micro-blogging services and location-based social networks, such as Twitter, Weibo, and Foursquare, enable users to post short messages with timestamps and geographical annotations. The rich spatial-temporal-semantic information of individuals embedded in these geo-annotated short messages provides exciting opportunity to develop many context-aware applications in ubiquitous computing environments. Example applications include contextual recommendation and contextual search. To obtain accurate recommendations and most relevant search results, it is important to capture users’ contextual information (e.g., time and location) and to understand users’ topical interests and intentions. While time and location can be readily captured by smartphones, understanding user’s interests and intentions calls for effective methods in modeling user mobility behavior. Here, user mobility refers to who visits which place at what time for what activity . That is, user mobility behavior modeling must consider user (Who), spatial (Where), temporal (When), and activity (What) aspects. Unfortunately, no previous studies on user mobility behavior modeling have considered all of the four aspects jointly, which have complex interdependencies. In our preliminary study, we propose the first solution named W 4 (short for Who, Where, When, and What) to discover user mobility behavior from the four aspects. In this article, we further enhance W 4 and propose a nonparametric Bayesian model named EW 4 (short for Enhanced W 4 ). EW 4 requires no parameter tuning and achieves better results over W 4 in our experiments. Given some of the four aspects of a user (e.g., time), our model is able to infer information of the other aspects (e.g., location and topical words). Thus, our model has a variety of context-aware applications, particularly in contextual search and recommendation. Experimental results on two real-world datasets show that the proposed model is effective in discovering users’ spatial-temporal topics. The model also significantly outperforms state-of-the-art baselines for various tasks including location prediction for tweets and requirement-aware location recommendation.

Wikification, which stands for the process of linking terms in a plain text document to Wikipedia articles which represent the correct meanings of the terms, can be thought of as a generalized Word Sense Disambiguation problem. It disambiguates multi-word expressions (MWEs) in addition to single words. Existing Wikification techniques either models the context of a given term as well as the Wikipedia article as bags of words, or compute global constraints among Wikipedia concepts by the link graph or link distributions. The first method doesn't achieve good results because the MWEs can have very different meanings than its constituent words which themselves are ambiguous. The second method doesn't produce high accuracy because the link structure or link distribution is often biased or incomplete by themselves due to the fact that Wikipedia pages are often sparsely linked. In this paper, we present a simple but powerful framework of sense disambiguation using co-occurrences of Wikipedia links in the Wikipedia corpus. We propose an iterative method to enrich the sparsely-linked articles by adding more links and then use the resulting link co-occurrence matrix to disambiguate an input document by a sliding window algorithm. Our prototype system achieves 89.97% precision and 76.43% recall on average for three benchmark data and compares favorably against four state-of-the-art wikification techniques.

Clustering of images from search results can improve the user experience of image search. Most of the existing systems use both visual features and surrounding texts as signals for clustering while this paper demonstrates the use of an external knowledge base to make better sense out of the text signals in a prototype system called CISC. Once we understand the semantics of the text better, the result of the clustering is significantly improved. In addition to clustering the images by their semantic entities, our system can also conceptualize each image cluster into a set of concepts to represent the meaning of the cluster.