The widespread dissemination and misleading impact of fake news on the web have become a significant concern for the public and the government. Discovering fake news is crucial for ensuring that users receive authentic information and maintaining social harmony. However, most existing entity-based fake news detection methods have two issues: i) methods for acquiring additional information through entities lack flexibility and real-time capabilities. ii) approaches using entities to capture news semantics have not adequately revealed the interactions between words in the text. To address these issues, we propose aMulti-graphSemantic-awareAdaptive Graph ConvolutionalNetwork (MgSAN), which comprehensively captures the semantic information of news texts by constructing multiple semantic graphs and learns the features from these graph structures using an adaptive graph convolutional network (SwiGCN). Specifically, we design a global semantic interaction graph to capture the complex interactions between words, generating a comprehensive textual semantic representation. We also employ an entity-noun relationship graph to mine deep semantic associations, enhancing the model's understanding of fine-grained textual deep meanings. Additionally, we develop an adaptive graph convolutional network to effectively extract and aggregate feature information from different graph structures. Finally, we introduce a fusion module to integrate both global and local fine-grained semantic information, forming a rich composite semantic representation, thereby improving the effectiveness of fake news detection. Extensive experimental results on three public benchmark datasets verify the effectiveness and superior performance of MgSAN, outperforming state-of-the-art detection models.

In real-world scenarios, a large amount of noise in user historical behaviors obstructs the reflection of their genuine interests. The long-tail distribution of user-item interactions also makes it difficult to capture interest evolution patterns from historical sequences. Moreover, as user behavior sequences continue to grow, solely relying on conventional sequence models is insufficient to extract user interest information and learn accurate sequence representations, thus limiting recommendation accuracy. To address these issues, we propose a self-supervised graph neural sequential recommendation model called LS4SRec, which disentangles users’ long- and short-term interests. Specifically, LS4SRec constructs two independent interest encoders to extract users’ long- and short-term interests. By utilizing the global user behavior sequence graph WITG to provide additional collaborative signals for each interaction sequence, we alleviate the issue of data sparsity. Subsequently, contrastive learning is applied to WITG to remove noise information and enhance the sequence representation. Further, interest allocation matrices and sequence models are utilized to model users’ interest evolution patterns. Finally, we introduce sequence graph data augmentation methods and long- and short-term interest pseudo-label construction methods to generate unsupervised signals that assist in model training. Extensive experiments conducted on real-world data validate the effectiveness of our proposed model. Our model implementation codes are available at the link https://github.com/jiubaoyibao/LS4SRec .

Nowadays, short-form videos (SVs) are essential to web information acquisition and sharing in our daily life. The prevailing use of SVs to spread emotions leads to the necessity of conducting video emotion analysis (VEA) towards SVs. Considering the lack of SVs emotion data, we introduce a large-scale dataset named eMotions, comprising 27,996 videos. Meanwhile, we alleviate the impact of subjectivities on labeling quality by emphasizing better personnel allocations and multi-stage annotations. In addition, we provide the category-balanced and test-oriented variants through targeted data sampling. Some commonly used videos, such as facial expressions, have been well studied. However, it is still challenging to analysis the emotions in SVs. Since the broader content diversity brings more distinct semantic gaps and difficulties in learning emotion-related features, and there exists local biases and collective information gaps caused by the emotion inconsistence under the prevalently audio-visual co-expressions. To tackle these challenges, we present an end-to-end audio-visual baseline AV-CANet which employs the video transformer to better learn semantically relevant representations. We further design the Local-Global Fusion Module to progressively capture the correlations of audio-visual features. The EP-CE Loss is then introduced to guide model optimization. Extensive experimental results across seven datasets demonstrate the effectiveness of AV-CANet, while providing broad insights for future works. Besides, we explore the key components of AV-CANet by ablation studies. Datasets and code are released at https://github.com/XuecWu/eMotions.

In recent years, the next-POI recommendation has become a trending research topic in the field of trajectory data mining. For protection of user privacy, users’ complete GPS trajectories are difficult to obtain. The check-in information posted by users on social networks has become an important data source for Spatio-temporal Trajectory research. However, state-of-the-art methods neglect the social meaning and the information dissemination function of check-in behavior. The social meaning is an important reason why users are willing to post check-in on social networks, and the information dissemination function means, users can affect each other’s behavior by check-ins. The above characteristics of the check-in behavior make it different from the visiting behavior. We consider a new problem of predicting the next check-in behavior including the check-in time, the POI (point-of-interest) where the check-in is located, functional semantics of the POI, and so on. To solve the proposed problem, we build a multi-task learning model called DPMTM, and a pre-training module is designed to extract dynamic social semantics of check-in behaviors. Our results show that the DPMTM model works well in the check-in behavior problem.

Tenuous subgraph finding aims to detect a subgraph with few social interactions and weak relationships among nodes. Despite significant efforts made on this task, they are mostly carried out in view of graph-structured data. These methods depend on calculating the shortest path and need to enumerate all the paths between nodes, which suffer the combinatorial explosion. Moreover, they all lack the integration of neighborhood information. To this end, we propose a novel model named Graph Neural Network with Motif-aware for tenuous subgraph finding (GNNM), a neighborhood aggregation-based GNN framework that can capture the latent relationship between nodes. We design a GNN module to project nodes into a low-dimensional vector combining the higher-order correlation within nodes based on a motif-aware module. Then we design greedy algorithms in vector space to obtain a tenuous subgraph whose size is greater than a specified constraint. Particularly, considering that existing evaluation indicators cannot capture the latent friendship between nodes, we introduce a novel Potential Friend concept to measure the tenuity of a graph from a new perspective. Experimental results on the real-world and synthetic datasets demonstrate that our proposed method GNNM outperforms existing algorithms in efficiency and subgraph quality.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement, i.e. events are scheduled at the reasonable time to attract maximum number of participants. Existing approaches usually focus on assigning a set of events organized by the same group to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this paper, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. Unfortunately, we find that the PETA problem is NP-hard due to the global conflict constraints on events. Thus, we propose design a greedy algorithm and two approximation algorithms to solve the PETA problem. Finally, we conduct extensive experiments on both real and synthetic datasets to test the effectiveness and efficiency of the proposed algorithms.

Online Event-based social networks (EBSNs), such as Meetup and Whova, which provide platforms for users to publish, arrange and participate in events, have become increasingly popular. A major challenge for managing EBSNs is to generate the most satisfactory event arrangement. Existing approaches usually focus on assigning a set of events organized to time intervals, but ignore the competitive relationships among different event organizers, which will lead to event time allocations unacceptable to organizers. Thus, a more intelligent EBSNs platform that allocates social events properly in a global view (i.e. the perspective of platform) is desired. In this work, we first formally define the problem of Platform-oriented Event Time Allocation (PETA), which contains two parts: the prediction of event feasible time period and the event time allocation. We propose a method to calculate event feasible time period based on event time prediction, and design a greedy algorithm and two approximation algorithms to solve the PETA problem. Extensive experiments on both real and synthetic datasets demonstrate that the proposed algorithms have high effectiveness and efficiency.

Location prediction has attracted much attention due to its important role in many location-based services, including taxi services, route navigation, traffic planning, and location-based advertisements. Traditional methods only use spatial-temporal trajectory data to predict where a user will go next. The divorce of semantic knowledge from the spatial-temporal one inhibits our better understanding of users’ activities. Inspired by the architecture of Long Short Term Memory (LSTM), we design ST-LSTM, which draws on semantic trajectories to predict future locations. Semantic data add a new dimension to our study, increasing the accuracy of prediction. Since semantic trajectories are sparser than the spatial-temporal ones, we propose a strategic filling algorithm to solve this problem. In addition, as the prediction is based on the historical trajectories of users, the cold-start problem arises. We build a new virtual social network for users to resolve the issue. Experiments on two real-world datasets show that the performance of our method is superior to those of the baselines.

Graph representation learning aims at learning low-dimension representations for nodes in graphs, and has been proven very useful in several downstream tasks. In this article, we propose a new model, Graph Community Infomax (GCI), that can adversarial learn representations for nodes in attributed networks. Different from other adversarial network embedding models, which would assume that the data follow some prior distributions and generate fake examples, GCI utilizes the community information of networks, using nodes as positive(or real) examples and negative(or fake) examples at the same time. An autoencoder is applied to learn the embedding vectors for nodes and reconstruct the adjacency matrix, and a discriminator is used to maximize the mutual information between nodes and communities. Experiments on several real-world and synthetic networks have shown that GCI outperforms various network embedding methods on community detection tasks.

Community detection aims to partition network nodes into a set of clusters, such that nodes are more densely connected to each other within the same cluster than other clusters. For attributed networks, apart from the denseness requirement of topology structure, the attributes of nodes in the same community should also be homogeneous. Network embedding has been proved extremely useful in a variety of tasks, such as node classification, link prediction, and graph visualization, but few works dedicated to unsupervised embedding of node features specified for clustering task, which is vital for community detection and graph clustering. By post-processing with clustering algorithms like k -means, most existing network embedding methods can be applied to clustering tasks. However, the learned embeddings are not designed for clustering task, they only learn topological and attributed information of networks, and no clustering-oriented information is explored. In this article, we propose an algorithm named Network Embedding for node Clustering (NEC) to learn network embedding for node clustering in attributed graphs. Specifically, the presented work introduces a framework that simultaneously learns graph structure-based representations and clustering-oriented representations together. The framework consists of the following three modules: graph convolutional autoencoder module, soft modularity maximization module, and self-clustering module. Graph convolutional autoencoder module learns node embeddings based on topological structure and node attributes. We introduce soft modularity, which can be easily optimized using gradient descent algorithms, to exploit the community structure of networks. By integrating clustering loss and embedding loss, NEC can jointly optimize node cluster labels assignment and learn representations that keep local structure of network. This model can be effectively optimized using stochastic gradient algorithm. Empirical experiments on real-world networks and synthetic networks validate the feasibility and effectiveness of our algorithm on community detection task compared with network embedding based methods and traditional community detection methods.

Destination prediction is an essential task in various mobile applications and up to now many methods have been proposed. However, existing methods usually suffer from the problems of heavy computational burden, data sparsity, and low coverage. Therefore, a novel approach named DestPD is proposed to tackle the aforementioned problems. Differing from an earlier approach that only considers the starting and current location of a partial trip, DestPD first determines the most likely future location and then predicts the destination. It comprises two phases, the offline training and the online prediction. During the offline training, transition probabilities between two locations are obtained via Markov transition matrix multiplication. In order to improve the efficiency of matrix multiplication, we propose two data constructs, Efficient Transition Probability (ETP) and Transition Probabilities with Detours (TPD). They are capable of pinpointing the minimum amount of needed computation. During the online prediction, we design Obligatory Update Point (OUP) and Transition Affected Area (TAA) to accelerate the frequent update of ETP and TPD for recomputing the transition probabilities. Moreover, a new future trajectory prediction approach is devised. It captures the most recent movement based on a query trajectory. It consists of two components: similarity finding through Best Path Notation (BPN) and best node selection. Our novel BPN similarity finding scheme keeps track of the nodes that induces inefficiency and then finds similarity fast based on these nodes. It is particularly suitable for trajectories with overlapping segments. Finally, the destination is predicted by combining transition probabilities and the most probable future location through Bayesian reasoning. The DestPD method is proved to achieve one order of cut in both time and space complexity. Furthermore, the experimental results on real-world and synthetic datasets have shown that DestPD consistently surpasses the state-of-the-art methods in terms of both efficiency (approximately over 100 times faster) and accuracy.

Predicting the bike demand can help rebalance the bikes and improve the service quality of a bike sharing system. A lot of work focuses on predicting the bike demand for all the stations. It is not necessary because the travel cost of rebalance operations increases sharply as the number of stations increases. In this paper, we take more attention to those stations with higher bike demand, which are called "central stations" in the following narrative. We propose a framework to predict the hourly bike demand based on the central stations we define. Firstly, we propose a novel clustering algorithm to assign different types of stations into each cluster. Secondly, we propose a hierarchical prediction model to predict the hourly bike demand for every cluster and each central station progressively. The experimental results on the NYC Citi Bike system show the advantages of our approach to these problems.

Similarity measure is one of the fundamental task in heterogeneous information network (HIN) analysis. It has been applied to many areas, such as product recommendation, clustering, and Web search. Most of the existing metrics can provide personalized services for users by taking a meta-path or meta-structure as input. However, these metrics may highly depend on the user-specified meta-path or meta-structure. In addition, users must know how to select an appropriate meta-path or meta-structure. In this article, we propose a novel similarity measure in HINs, called Recurrent Meta-Structure (RecurMS)-based Similarity (RMSS). The RecurMS as a schematic structure in HINs provides a unified framework for integrating all of the meta-paths and meta-structures, and can be constructed automatically by means of repetitively traversing the network schema. In order to formalize the semantics, the RecurMS is decomposed into several recurrent meta-paths and recurrent meta-trees, and we then define the commuting matrices of the recurrent meta-paths and meta-trees. All of these commuting matrices are combined together according to different weights. We propose two kinds of weighting strategies to determine the weights. The first is called the local weighting strategy that depends on the sparsity of the commuting matrices, and the second is called the global weighting strategy that depends on the strength of the commuting matrices. As a result, RMSS is defined by means of the weighted summation of the commuting matrices. Note that RMSS can also provide personalized services for users by means of the weights of the recurrent meta-paths and meta-trees. Experimental evaluations show that the proposed RMSS is robust and outperforms the existing metrics in terms of ranking and clustering task.

Majority of taxi recommender systems mainly focused on satisfaction of passengers without considering fairness in assignment of taxi drivers. In this paper we propose a balanced assignment mechanism for online taxi recommendation (BAMOTR). BAMOTR provides a mechanism for fair assignment of drivers at some locations with specific routes to pick up passengers and ensures a short waiting time for passengers. Fair assignment is intended to minimize the differences in income among the taxi drivers. Analysis shows out that fair assignment of drivers and shortening the time the passenger wait before pick up is a trade-off problem. In this paper, we set a regulatory factor that can adjust the trade-off between fair assignment of drivers and shortening of waiting time of passengers. We also propose an efficient range refinement algorithm to solve online taxi recommendation problem in BAMOTR. It is theoretically and experimentally proved that range refinement algorithm ensures the same recommendation result as brute-force algorithm, however it greatly reduces the time overhead. We validate the performances of BAMOTR with extensive evaluations. Experimental results show that BAMOTR achieve better recommendation fairness than compared approaches and guarantee a short waiting time for passengers to be picked up.

The problem of mobile sequential recommendation is to suggest a route connecting a set of pick-up points for a taxi driver so that he/she is more likely to get passengers with less travel cost. Essentially, a key challenge of this problem is its high computational complexity. In this paper, we propose a novel dynamic programming based method to solve the mobile sequential recommendation problem consisting of two separate stages: an offline pre-processing stage and an online search stage. The offline stage pre-computes potential candidate sequences from a set of pick-up points. A backward incremental sequence generation algorithm is proposed based on the identified iterative property of the cost function. Simultaneously, an incremental pruning policy is adopted in the process of sequence generation to reduce the search space of the potential sequences effectively. In addition, a batch pruning algorithm is further applied to the generated potential sequences to remove some non-optimal sequences of a given length. Since the pruning effectiveness keeps growing with the increase of the sequence length, at the online stage, our method can efficiently find the optimal driving route for an unloaded taxi in the remaining candidate sequences. Moreover, our method can handle the problem of optimal route search with a maximum cruising distance or a destination constraint. Experimental results on real and synthetic data sets show that both the pruning ability and the efficiency of our method surpass the state-of-the-art methods. Our techniques can therefore be effectively employed to address the problem of mobile sequential recommendation with many pick-up points in real-world applications.

Clustering is an important technique for mining the intrinsic community structures in networks. The density-based network clustering method is able to not only detect communities of arbitrary size and shape, but also identify hubs and outliers. However, it requires manual parameter specification to define clusters, and is sensitive to the parameter of density threshold which is difficult to determine. Furthermore, many real-world networks exhibit a hierarchical structure with communities embedded within other communities. Therefore, the clustering result of a global parameter setting cannot always describe the intrinsic clustering structure accurately. In this paper, we introduce a novel density-based network clustering method, called graph-skeleton-based clustering (gSkeletonClu). By projecting an undirected network to its core-connected maximal spanning tree, the clustering problem can be converted to detect core connectivity components on the tree. The density-based clustering of a specific parameter setting and the hierarchical clustering structure both can be efficiently extracted from the tree. Moreover, it provides a convenient way to automatically select the parameter and to achieve the meaningful cluster tree in a network. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu for effective and efficient density-based clustering.

Ranking is a core problem for information retrieval since the performance of the search system is directly impacted by the accuracy of ranking results. Ranking model construction has been the focus of both the fields of information retrieval and machine learning, and learning to rank in particular has attracted much interest. Many ranking models have been proposed, for example, RankSVM is a state-of-the-art method for learning to rank and has been empirically demonstrated to be effective. However, most of the proposed methods do not consider about the significant differences between queries, only resort to a single function in ranking. In this paper, we present a novel ranking model named QoRank, which performs the learning task dependent on queries. We also propose a LSE (least-squares estimation) -based weighted method to aggregate the ranking lists produced by base decision functions as the final ranking. Comparison of QoRank with other ranking techniques is conducted, and several evaluation criteria are employed to evaluate its performance. Experimental results on the LETOR OHSUMED data set show that QoRank strikes a good balance of accuracy and complexity, and outperforms the baseline methods. © 2010 Wiley Periodicals, Inc.

Community detection is an important task for mining the structure and function of complex networks. Generally, there are several different kinds of nodes in a network which are cluster nodes densely connected within communities, as well as some special nodes like hubs bridging multiple communities and outliers marginally connected with a community. In addition, it has been shown that there is a hierarchical structure in complex networks with communities embedded within other communities. Therefore, a good algorithm is desirable to be able to not only detect hierarchical communities, but also identify hubs and outliers. In this paper, we propose a parameter-free hierarchical network clustering algorithm SHRINK by combining the advantages of density-based clustering and modularity optimization methods. Based on the structural connectivity information, the proposed algorithm can effectively reveal the embedded hierarchical community structure with multiresolution in large-scale weighted undirected networks, and identify hubs and outliers as well. Moreover, it overcomes the sensitive threshold problem of density-based clustering algorithms and the resolution limit possessed by other modularity-based methods. To illustrate our methodology, we conduct experiments with both real-world and synthetic datasets for community detection, and compare with many other baseline methods. Experimental results demonstrate that SHRINK achieves the best performance with consistent improvements.

Community detection is an important task for mining the structure and function of complex networks. Many pervious approaches are difficult to detect communities with arbitrary size and shape, and are unable to identify hubs and outliers. A recently proposed network clustering algorithm, SCAN, is effective and can overcome this difficulty. However, it depends on a sensitive parameter: minimum similarity threshold ε, but provides no automated way to find it. In this paper, we propose a novel density-based network clustering algorithm, called gSkeletonClu (graph-skeleton based clustering). By projecting a network to its Core-Connected Maximal Spanning Tree (CCMST), the network clustering problem is converted to finding core-connected components in the CCMST. We discover that all possible values of the parameter ε lie in the edge weights of the corresponding CCMST. By means of tree divisive or agglomerative clustering, our algorithm can find the optimal parameter ε and detect communities, hubs and outliers in large-scale undirected networks automatically without any user interaction. Extensive experiments on both real-world and synthetic networks demonstrate the superior performance of gSkeletonClu over the baseline methods.

Ranking is a central problem for information retrieval systems, because the performance of an information retrieval system is mainly evaluated by the effectiveness of its ranking results. Learning to rank has received much attention in recent years due to its importance in information retrieval. This paper focuses on learning to rank in document retrieval and presents a ranking model named OrdRank that ranks documents with ordered multiple hyperplanes. Comparison of OrdRank with other state-of-the-art ranking techniques is conducted and several evaluation criteria are employed to evaluate its performance. Experimental results on the OHSUMED dataset show that OrdRank outperforms other methods, both in terms of quality of ranking results and efficiency.