The epidemic of infectious diseases has a significant impact on society, the economy, and people’s lives. Social media, with its high user participation and rapid information dissemination, plays a crucial role in shaping public opinion. Fine-grained sentiment analysis of public opinion on infectious diseases can provide valuable insights for improving the quality of public services. However, there are few relevant studies on Chinese data due to language complexity and low resources. Moreover, most of the existing approaches utilize the Graph Neural Network (GCN) method by syntactic dependency trees to construct graphs of text, which ignore the potential link relationships between aspects and words. Therefore, to address this limitation, in this article, we propose a new method based on GCN using aspect-specific heterogeneous graphs, named ASHGCN, which combines BiLSTM, heterogeneous graphs, GCN, the mask and the attention mechanism. We mine social media posts related to COVID-19 for aspect-based sentiment analysis task (ABSA) for ten aspect entity types in both Chinese and English data. The heterogeneous graph is designed with two node types (aspect nodes and non-aspect nodes) and four edge connection types, including various relationships between aspect entities, and between aspect entities and non-aspect entities. In addition, we release a Chinese dataset and an English dataset that include medical and named entities, along with corresponding sentiment labels. Experiments on our datasets, as well as two public datasets, demonstrate that our method greatly improves performance in the ABSA task. Ablation experiments and case studies further support the effectiveness of the proposed approach.

Core decomposition is a well-established graph mining problem that involves a widely-used subgraph model k-core in network analysis. Solutions to this problem have been developed using the Peel and Index paradigms. However, existing studies have not effectively harnessed GPU performance to expedite core decomposition, despite the growing need for enhanced performance. This paper introduces an efficient GPU acceleration framework, PICO, for both the Peel and Index paradigms of k-core decomposition. We propose a Peel-based algorithm, named PeelOne, which reduces the frontiers searching cost and redundant memory footprint of the frontiers by virtual small graph transformation and kernel fusion. Moreover, we design a GPU-friendly hybrid frontier queues architecture to balance the performance and the memory usage and quickly handle any overflow of the queues. We also propose an Index-based algorithm, named HistoCore, which addresses the issue of extensive redundant computations across both vertices and edges. Extensive experiments on NVIDIA RTX 3090 GPU show that PeelOne outperforms the SOTA Peel-based algorithm by 1.4 × ∼3.5 × speedup, and HistoCore outperforms the existing Index-based algorithm by 9.4 × ∼26.3 × speedup.

The recent prosperity of temporal graph research redefines many traditional concepts on static graphs, such as triangle, motif,$k$-core, etc. Inspired by that, we propose a novel$(k, \delta)$-truss on temporal graphs, which requires its triangles to exist in short enough time windows ever. The$(k,\delta)$-truss satisfies both static and temporal cohesion, while the original$k$-truss is its special case when$\delta=\infty$. In order to address the$(k, \delta)$-truss query, we propose both index-free and index-based approaches. By leveraging the dual containment relation on$(k, \delta)$-trusses, our indexes can compress all$(k, \delta)$-trusses losslessly into map or tree structures with dramatically less space, so that a specific$(k,\ \delta)$-truss can be retrieved from indexes in the optimal time. To enable our index to scale to large temporal graphs, we develop two index construction algorithms that can reduce redundant computation significantly, based on truss decomposition and truss maintenance respectively. The experimental results demonstrate that index-based approaches process queries in interactive time and outperform the index-free approach by 2~4 orders of magnitude, while indexes achieve compression ratios up to 10-4.

Core decomposition is a well-established graph mining algorithm in network analysis that involves the k-core subgraph model. However, most existing works on GPUs perform inefficiency due to the heavy search cost for identifying the sparse frontiers throughout the entire vertex space, which is caused by the minimum-degree dependency. Existing works design multiple block queues to partially reduce the cost caused by the sparse frontier search. Nonetheless, the queues consume excessive global memory to avoid queue overflow, leading to the decreased scalability. To address these problems, this paper proposes a space-efficient and dependency-aware GPU parallel framework, SpeedCore, for core decomposition. A theoretically verified virtual graph method is proposed to compress the large sparse search space into a small and dense format. A cache-centric hybrid queue method with a task granularity control strategy at the block level is proposed to restrict memory usage and prevent overflow. Furthermore, SpeedCore proposes a context-based binary search method to accelerate the graph degeneracy problem, which is derived from core decomposition. Extensive experiments on the NVIDIA RTX 3090 GPU show that SpeedCore outperforms the state-of-the-art algorithm by a 2.2 × average speedup. Moreover, SpeedCore can still maintain almost the same performance with low memory consumption. To solve the graph degeneracy problem, SpeedCore achieves a 7.5 × average speedup.

Community detection is a fundamental yet important task for characterizing and understanding the structure of attributed graphs. Existing methods mainly focus on the structural tightness and attribute similarity among nodes in a community. However, grouping numerous semantically homogeneous nodes will result in information cocoons and thus reduce the robustness of community structure and the efficiency of node collaboration in real-world applications, such as recommendation systems and collaboration networks. Since nodes with closer connections tend to be more similar, finding communities with dense structures and diverse attributes poses great challenges to mining latent relationships between the graph structure and attribute distribution. To our best knowledge, very little research has been conducted to address this challenge. In this article, we propose a novel three-view graph attention neural networks (TvGANN) model to formally address the attribute diversity aware community detection problem. TvGANN reveals correlations between the graph structure and attributes distribution from the perspective of node organization, attribute co-occurrence, and the node-attribute interaction. It effectively captures structural features and attributes distribution by feeding a structural network and an attribute co-occurrence network into graph attention modules through the encoder–decoder framework. It also learns heterogeneous information by feeding a network into a meta-node attention module. Then, it fuzes the three modules and clusters the embedding representations through a Student's t -distribution approach, which iteratively refines the clustering results. The experiments show that our method not only improves the quality in dense community detection but also performs efficiently for attributed graphs.

Incremental maximal clique enumeration (IMCE), which maintains maximal cliques in dynamic graphs, is a fundamental problem in graph analysis. A maximal clique has a solid descriptive power of dense structures in graphs. Real-world graph data is often large and dynamic. Studies on IMCE face significant challenges in the efficiency of incremental batch computation and hybrid edge changes. Moreover, with growing graph sizes, new requirements occur on indexing global maximal cliques and obtaining maximal cliques under specific vertex scope constraints. This work presents a new data structure SOMEi to maintain intermediate maximal cliques during construction. SOMEi serves as a space-efficient index to retrieve scope-constrained maximal cliques on the fly. Based on SOMEi, we design a procedure-oriented IMCE algorithm to deal with hybrid edge changes within a unified algorithm framework. In particular, the algorithm is able to process a large batch of edge changes and significantly improve the average processing time of a single edge change through an efficient pruning strategy. Experimental results on real and synthetic graph data demonstrate that the proposed algorithm outperforms all the baselines and achieves good efficiency through pruning.

In recent years, hypergraph analysis have attracted increasing attention due to their ability to model complex data correlation, with hypergraph clustering being one of the most important tasks. However, when the scale of hypergraph is large enough, clustering is difficult based on global consistency. Existing flow-based hypergraph local clustering methods have good theoretical cut improvements and runtime guarantees. However, these methods exhibit poor performance when the initial reference node set is small and are prone to causing the output set to shrink into a small subset, resulting in local minima. To address this issue, we propose the Penalized Flow Hypergraph Local Clustering(PFHLC) and provide new conductance guarantees and runtime analyses for our method. First, we use the random walk method to grow the initial seed set, and introduce the random walk information of nodes as penalized flow into the flow-based framework to optimize the output. Second, we propose a generalized objective function containing random walk information, which takes full advantage of the semi-supervised information of the target cluster to protect important nodes. This feature can avoid the local minima of previous flow-based methods. Importantly, our method is strongly-local and can run efficiently on large-scale hypergraphs. We contribute a real-world dataset and the experiments on real-world large-scale datasets show that PFHLC achieves the state-of-the-art significantly.

Graph Convolutional Network (GCN) is an important method for learning graph representations of nodes. For large-scale graphs, the GCN could meet with the neighborhood expansion phenomenon, which makes the model complexity high and the training time long. An efficient solution is to adopt graph sampling techniques, such as node sampling and random walk sampling. However, the existing sampling methods still suffer from aggregating too many neighbor nodes and ignoring node feature information. Therefore, in this paper, we propose a new subgraph sampling method, namely, Similarity-Aware Random Walk (SARW), for GCN with large-scale graphs. A novel similarity index between two adjacent nodes is proposed, describing the relationship of nodes with their neighbors. Then, we design a sampling probability expression between adjacent nodes using node feature information, degree information, neighbor set information, etc. Moreover, we prove the unbiasedness of the SARW-based GCN model for node representations. The simplified version of SARW (SSARW) has a much smaller variance, which indicates the effectiveness of our subgraph sampling method in large-scale graphs for GCN learning. Experiments on six datasets show our method achieves superior performance over the state-of-the-art graph sampling approaches for the large-scale graph node classification task.

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for images sharing similar content with the query WSI, especially in the case-based diagnosis. While slide-level retrieval could be more intuitive and practical in clinical applications, most methods are designed for patch-level retrieval. A few recently unsupervised slide-level methods only focus on integrating patch features directly, without perceiving slide-level information, and thus severely limits the performance of WSI retrieval. To tackle the issue, we propose a High-Order Correlation-Guided Self-Supervised Hashing-Encoding Retrieval (HSHR) method. Specifically, we train an attention-based hash encoder with slide-level representation in a self-supervised manner, enabling it to generate more representative slide-level hash codes of cluster centers and assign weights for each. These optimized and weighted codes are leveraged to establish a similarity-based hypergraph, in which a hypergraph-guided retrieval module is adopted to explore high-order correlations in the multi-pairwise manifold to conduct WSI retrieval. Extensive experiments on multiple TCGA datasets with over 24,000 WSIs spanning 30 cancer subtypes demonstrate that HSHR achieves state-of-the-art performance compared with other unsupervised histology WSI retrieval methods.

The misinformation detection systems are increasingly important in E-commerce management, which detect misinformation on the commodity display page. Misinformation in E-commerce is usually presented as a mismatch between multi-modal information, the detection systems need to find the misinformation across the multi-model information. Furthermore, with the development of E-commerce globalization, we hope to deploy the system in the international E-commerce platform, which may face the difficulty caused by multi-lingual data. To this end, we propose aCross-lingualMulti-modalMisinformationDetection (CMMD) framework for E-commerce management. The CMMD framework includes a word alignment network that embeds information from different languages into the same feature space and a multimodal fusion structure that fuses text representations and image representations through two self-supervised tasks. With the cooperation of these two modules, the CMMD model could extract rich cross-lingual multi-modal features to achieve accurate misinformation detection. We conduct experiments on the public multi-modal dataset and further apply the proposed CMMD to the real-world E-commerce international platform. The experimental results show that the proposed framework CMMD achieves better performance on the public dataset than some benchmarks and gets satisfactory results in real-world E-commerce management.

Streaming computation for large graphs on parallel systems faces challenges in task decomposition, data skew, and resource scheduling. In this work, we propose a general parallel streaming framework for the node-centered graph algorithms to improve the computation efficiency. We construct the parallel procedure of the incremental maximal clique enumeration (IMCE) task and accelerate the incremental Candidate Map Constructor (CMC) algorithm through the framework for large-scale streaming graphs. Experimental results on three large real-world graphs show the framework’s positive effect on the algorithm’s execution time.

Data mining in infectious disease pandemic scenarios is a complex giant task involving data from various fields and requirements of real-time and dynamic. In this paper, we propose a graph mining system for the infectious disease pandemic, IDGMS, with one-stop, dynamic, and interactive characteristics. The system has been applied to solve problems from three view scales and performs well. The system is constructed as a loose coupling structure at the front and back ends and can be extended to more graph mining issues. To the best of our knowledge, we are the first graph system especially targeting data mining of infectious diseases.

The advancement of science and technology provides the possibility of personalized intelligent education. Representation learning of students’ behavior data is challenging because whether time sequences and interactive behaviors or the correlation between knowledge points and students carrying important information. Some researchers propose knowledge tracing to provide ideas for solving this dilemma. However, existing knowledge tracing methods are divided into machine learning and deep learning. Machine learning-based methods require manual feature extraction and a large amount of prior knowledge. Although deep learning-based methods can automatically extract features, most methods either only use the time series information of the data, or use the association between knowledge points. All the methods ignore the association between knowledge points and students. To fill this gap, we propose a Gated Heterogeneous Graph Convolutional Network (GHGCN) model. We utilize the encoder-decoder framework to predict student performance using the representations of nodes, which is learned from heterogeneous convolutional networks and gate recurrent unit. To validate the effectiveness of the proposed GHGCN model, we conduct the experiments on three public datasets: Simulated Data, Assistments 2009, and Assistments 2015. The results indicate that our method can achieve better performance compared with state-of-the-art algorithms.

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.

In the emerging business of food delivery, rider traffic accidents raise financial cost and social traffic burden. Although there has been much effort on traffic accident forecasting using temporal-spatial prediction models, none of the existing work studies the problem of detecting the takeaway rider accidents based on food delivery trajectory data. In this article, we aim to detect whether a takeaway rider meets an accident on a certain time period based on trajectories of food delivery and riders’ contextual information. The food delivery data has a heterogeneous information structure and carries contextual information such as weather and delivery history, and trajectory data are collected as a spatial-temporal sequence. In this article, we propose a TakeAway Rider Accident detection fusion network TARA-Net to jointly model these heterogeneous and spatial-temporal sequence data. We utilize the residual network to extract basic contextual information features and take advantage of a transformer encoder to capture trajectory features. These embedding features are concatenated into a pyramidal feed-forward neural network. We jointly train the above three components to combine the benefits of spatial-temporal trajectory data and sparse basic contextual data for early detecting traffic accidents. Furthermore, although traffic accidents rarely happen in food delivery, we propose a sampling mechanism to alleviate the imbalance of samples when training the model. We evaluate the model on a transportation mode classification dataset Geolife and a real-world Ele.me dataset with over 3 million riders. The experimental results show that the proposed model is superior to the state-of-the-art.