Publications |

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

Mining Discriminative Salient Objects with Optimal Transport for Few-Shot Image Classification

Wed, 08 Jul 2026 00:00:00 +0000

FLINS-ISKE, 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

A Hypergraph-Based Framework for Exploratory Business Intelligence

Wed, 11 Mar 2026 00:00:00 +0000

arXiv preprint, 2026

Business Intelligence (BI) analysis is evolving towards Exploratory BI, an iterative, multi-round exploration paradigm where analysts progressively refine their understanding. However, traditional BI systems impose critical limits for Exploratory BI: heavy reliance on expert knowledge, high computational costs, static schemas, and lack of reusability. We present ExBI, a novel system that introduces the hypergraph data model with operators, including Source, Join, and View, to enable dynamic schema evolution and materialized view reuse. Using sampling-based algorithms with provable estimation guarantees, ExBI addresses the computational bottlenecks, while maintaining analytical accuracy. Experiments on LDBC datasets demonstrate that ExBI achieves significant speedups over existing systems: on average 16.21x (up to 146.25x) compared to Neo4j and 46.67x (up to 230.53x) compared to MySQL, while maintaining high accuracy with an average error rate of only 0.27% for COUNT, enabling efficient and accurate large-scale exploratory BI workflows.

Finding critical users in social networks with reinforcement learning

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

Information Sciences, 2026

AEFA: An Ensemble Framework for Fraud Detection in the Forex Market

Sat, 18 Oct 2025 00:00:00 +0000

International Conference on Advanced Data Mining and Applications, 2025

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.

Deep Overlapping Community Search via Subspace Embedding

Mon, 10 Feb 2025 00:00:00 +0000

ACM SIGMOD Conference, 2025

Overlapping Community Search (OCS) identifies nodes that interact with multiple communities based on a specified query. Existing community search approaches fall into two categories: algorithm-based models and Machine Learning-based (ML) models. Despite the long-standing focus on this topic within the database domain, current solutions face two major limitations: 1) Both approaches fail to address personalized user requirements in OCS, consistently returning the same set of nodes for a given query regardless of user differences. 2) Existing ML-based CS models suffer from severe training efficiency issues. In this paper, we formally redefine the problem of OCS. By analyzing the gaps in both types of approaches, we then propose a general solution for OCS named S parse S ubspace F ilter (SSF), which can extend any ML-based CS model to enable personalized search in overlapping structures. To overcome the efficiency issue in the current models, we introduce S implified M ulti-hop Attention N etworks (SMN), a lightweight yet effective community search model with larger receptive fields. To the best of our knowledge, this is the first ML-based study of overlapping community search. Extensive experiments validate the superior performance of SMN within the SSF pipeline, achieving a 13.73% improvement in F1-Score and up to 3 orders of magnitude acceleration in model efficiency compared to state-of-the-art approaches.

RPDN: An Effective Rating Pollution Attacks Detection Framework for Recommendation Systems

Wed, 01 Jan 2025 00:00:00 +0000

Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2025

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.

Neural Similarity Search on Supergraph Containment (Extended Abstract)

Mon, 13 May 2024 00:00:00 +0000

IEEE International Conference on Data Engineering, 2024

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. In other words, the goal is to determine if part of the query graph is the same as a smaller data graph. Existing algorithms construct the indices and adopt the filtering-and-verification framework, 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 our knowledge, no learning-based method is 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). NSS first learns the representations for query and data graphs and then efficiently conducts the supergraph search on the representation space, the complexity of which is linear to the number of data graphs. The carefully designed Wasserstein discriminator and reconstruction network enable NSS to capture better the interrelation, structural and label information between and within the query and data graphs. Experiments demonstrate that the NSS is up to 6 orders of magnitude faster than the state-of-the-art exact supergraph search algorithm in query processing and is more accurate than the other learning-based solutions.

SBGMN: A Multi-view Sign Prediction Network for Bipartite Graphs

Mon, 01 Jan 2024 00:00:00 +0000

Asia Pacific Web Conference (APWeb-WAIM), 2024

KGNIE: A Learning Method for Estimating Node Importance in Knowledge Graphs

Sun, 17 Dec 2023 00:00:00 +0000

IEEE International Conference on High Performance Computing and Communications, 2023

Estimating node importance is critical in graph data mining, benefiting various downstream applications such as social network analysis and recommendation systems. Existing approaches face challenges when dealing with complex knowledge graphs due to the abundance of predicate and entity information. This paper introduces KGNIE, an efficient knowledge graph node importance estimation network. KGNIE considers the rich predicate attributes and entity types, and utilizes local and global information encoders to generate node embeddings with different importance-related information. An attention-based fusion module is employed to balance the two encoders. A node importance decoder is proposed to map node embed dings to importance scores based on entity types. Furthermore, we introduce a margin ranking loss to determine relative node importance rankings. We conducted extensive experiments on real-world knowledge graphs, demonstrating that our model outperforms existing approaches across all evaluation metrics.

IFGNN: An Individual Fairness Awareness Model for Missing Sensitive Information Graphs

Mon, 06 Nov 2023 00:00:00 +0000

Australasian Database Conference, 2023

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.

Group-based Fraud Detection Network on e-Commerce Platforms

Fri, 04 Aug 2023 00:00:00 +0000

ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Along with the rapid technological and commercial innovation on the e-commerce platforms, there are an increasing number of frauds that bring great harm to these platforms. Many frauds are conducted by organized groups of fraudsters for higher efficiency and lower costs, which are also known as group-based frauds. Despite the high concealment and strong destructiveness of group-based fraud, there is no existing research work that 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 which we propose a novel end-to-end semi-supervised Group-based Fraud Detection Network (GFDN) to support such fraud detection in real-world applications. Experimental results on large-scale e-commerce datasets from Taobao and Bitcoin trading datasets show the superior effectiveness and efficiency of our proposed model for group-based fraud detection on bipartite graphs.

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.