Article-Journal |

SARL: A Scalable Attribute-Informed Representation Learning Framework for Bipartite Graphs

Sun, 13 Sep 2026 00:00:00 +0000

IEEE Transactions on Knowledge and Data Engineering, 2026

MGDN: A Graph of Graphs Neural Network for Malware Detection

Thu, 30 Apr 2026 00:00:00 +0000

IEEE Transactions on Knowledge and Data Engineering, 2026

Finding critical users in social networks with reinforcement learning

Fri, 13 Feb 2026 00:00:00 +0000

Information Sciences, 2026

RIDA: a robust attack framework on incomplete graphs

Thu, 22 May 2025 00:00:00 +0000

World Wide Web, 2025

IGFM: An Enhanced Graph Similarity Computation Method with Fine-Grained Analysis

Tue, 08 Apr 2025 00:00:00 +0000

Data Science and Engineering, 2025

Abstract In the rapidly advancing field of graph-based applications, accurate graph similarity computing (GSC) has become increasingly important. However, due to the complexity of graph structures, this task remains a challenge because of the intricate calculations involved. To solve the limitations of existing works, this paper introduces the Interpretable Graph Fusion Model (), a novel framework designed to enhance the accuracy and efficiency of graph similarity computation. Specifically, our model can fully utilize graph structure information and comprehensively assess graph similarity at both fine-grained and coarse-grained levels, ultimately achieving more accurate predictions. Experimented extensively across four real-world datasets, demonstrates a significant improvement over existing SOTA methods to solve the GSC challenge. In numerous experimental tests, our model shows performance improvements in terms of MSE (Mean Squared Error), ranging from 4.66% to as much as 56.92% compared to the second-best method.

Temporal Insights for Group-Based Fraud Detection on e-Commerce Platforms

Thu, 31 Oct 2024 00:00:00 +0000

IEEE Transactions on Knowledge and Data Engineering, 2024

Along with the rapid technological and commercial innovation on e-commerce platforms, an increasing number of frauds cause great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, no existing research can thoroughly exploit the information within the transaction networks of e-commerce platforms for group-based fraud detection. In this work, we analyze and summarize the characteristics of group-based frauds. Based on this, we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. In addition, we introduce a module namedTemporal Group Dynamics Analyzer(TGDA) that strengthens the ability to analyze temporal information on group fraudulent activity. Based on this, we built an enhanced model named TGFDN. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show our proposed model’s superior effectiveness and efficiency for group-based fraud detection on bipartite graphs.

FPGN: follower prediction framework for infectious disease prevention

Sat, 16 Sep 2023 00:00:00 +0000

World Wide Web, 2023

Abstract In recent years, how to prevent the widespread transmission of infectious diseases in communities has been a research hot spot. Tracing close contact with infected individuals is one of the most severe problems. In this work, we present a model called Follower Prediction Graph Network (FPGN) to identify high-risk visitors, which is known as follower prediction. The model is designed to identify visitors who may be infected with a disease by tracking their activities at the exact location of infected visitors. FPGN is inspired by the state-of-the-art temporal graph edge prediction algorithm TGN and draws on the shortcomings of existing algorithms. It utilizes graph structure information based on (

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β )-core, time interval statistics by using the statistics of timestamp information, and a GAT-based prediction module to achieve high accuracy in follower prediction. Extensive experiments are conducted on two real datasets, demonstrating the progress of FPGN. The experimental results show that FPGN can achieve the highest results compared with other SOTA baselines. Its AP scores are higher than 0.46, and its AUC scores are higher than 0.62.

Neural Similarity Search on Supergraph Containment

Thu, 25 May 2023 00:00:00 +0000

IEEE Transactions on Knowledge and Data Engineering, 2023

Supergraph search is a fundamental graph query processing problem. Supergraph search aims to find all data graphs contained in a given query graph based on the subgraph isomorphism. Existing algorithms construct the indices and adopt thefiltering-and-verificationframework which is usually computationally expensive and can cause redundant computations. Recently, various learning-based methods have been proposed for a good trade-off between accuracy and efficiency for query processing tasks. However, to the best of our knowledge, there is no learning-based method proposed for the supergraph search task. In this paper, we propose the first learning-based method for similarity search on supergraph containment, named Neural Supergraph similarity Search (NSS).NSSfirst learns the representations for query and data graphs and then efficiently conducts the supergraph search on the representation space whose complexity is linear to the number of data graphs. The carefully designed Wasserstein discriminator and reconstruction network enableNSSto better capture the interrelation, structural and label information between and within the query and data graphs. Experiments demonstrate that theNSSis up to 6 orders of magnitude faster than the state-of-the-art exact supergraph search algorithm in terms of query processing and more accurate compared to the other learning-based solutions.

Polarity-based graph neural network for sign prediction in signed bipartite graphs

Wed, 16 Feb 2022 00:00:00 +0000

World Wide Web, 2022

Abstract As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex structures with positive and negative links as well as bipartite settings, on which conventional graph analysis algorithms are no longer applicable. Previous works mainly focus on unipartite signed graphs or unsigned bipartite graphs separately. Several models are proposed for applications on the signed bipartite graphs by utilizing the heuristic structural information. However, these methods have limited capability to fully capture the information hidden in such graphs. In this paper, we propose the first graph neural network on signed bipartite graphs, namely Polarity-based Graph Convolutional Network (PbGCN), for sign prediction task with the help of balance theory. We introduce the novel polarity attribute to signed bipartite graphs, based on which we construct one-mode projection graphs to allow the GNNs to aggregate information between the same type nodes. Extensive experiments on five datasets demonstrate the effectiveness of our proposed techniques.

Bipartite graph capsule network

Mon, 14 Feb 2022 00:00:00 +0000

World Wide Web, 2022

Abstract Graphs have been widely adopted in various fields, where many graph models are developed. Most of previous research focuses on unipartite or homogeneous graph analysis. In this graphs, the relationships between the same type of entities are preserved in the graphs. Meanwhile, the bipartite graphs that model the complex relationships among different entities with vertices partitioned into two disjoint sets, are becoming increasing popular and ubiquitous in many real life applications. Though several graph classification methods on unipartite and homogenous graphs have been proposed by using kernel method, graph neural network, etc. However, these methods are unable to effectively capture the hidden information in bipartite graphs. In this paper, we propose the first bipartite graph-based capsule network, namely Bipartite Capsule Graph Neural Network (BCGNN), for the bipartite graph classification task. BCGNN exploits the capsule network and obtains information between the same type vertices in the bipartite graphs by constructing the one-mode projection. Extensive experiments are conducted on real-world datasets to demonstrate the effectiveness of our proposed method.