eRiskCom: an e-commerce risky community detection platform

Liu, Fanzhen; Zhao, Li; Wang, Baokun; Wu, Jia; Yang, Jian; Huang, Jiaming; Yiqing, Zhang; Wang, Weiqiang; Xue, Shan; Nepal, Surya; Sheng, Quan Z.

doi:10.1007/s00778-021-00723-z

Cited by 14 publications

(4 citation statements)

References 60 publications

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“…The continuous-valued feature for each node corresponds to the TF/IDF weighted word representation of each publication. These two networks are provided by GitHub 2 and LINQS 3 , respectively.…”

Section: Methodsmentioning

confidence: 99%

“…Networks provide a natural way to express the complex relationships in our daily life, such as scientific collaborations [1], friend interactions [2], information dissemination [3], and module associations [4]. Typically, the individuals and their relationships in real-world scenarios are represented as the nodes and edges in networks where each node is associated with one or more features characterizing the properties of the individual it corresponds.…”

Section: Introductionmentioning

confidence: 99%

“…http://snap.stanford.edu/data/index.html. 2 https://github.com/smileyan448/Sinanet 3. https://linqs.soe.ucsc.edu/data.…”

mentioning

confidence: 99%

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Triangle-oriented Community Detection considering Node Features and Network Topology

Gao¹,

Liang²,

Yuan³

et al. 2022

Preprint

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The joint use of node features and network topology to detect communities is called community detection in attributed networks. Most of the existing work along this line has been carried out through objective function optimization and has proposed numerous approaches. However, they tend to focus only on lower-order details, i.e., capture node features and network topology from node and edge views, and purely seek a higher degree of optimization to guarantee the quality of the found communities, which exacerbates unbalanced communities and free-rider effect. To further clarify and reveal the intrinsic nature of networks, we conduct triangle-oriented community detection considering node features and network topology. Specifically, we first introduce a trianglebased quality metric to preserve higher-order details of node features and network topology, and then formulate so-called two-level constraints to encode lower-order details of node features and network topology. Finally, we develop a local search framework based on optimizing our objective function consisting of the proposed quality metric and two-level constraints to achieve both nonoverlapping and overlapping community detection in attributed networks. Extensive experiments demonstrate the effectiveness and efficiency of our framework and its potential in alleviating unbalanced communities and free-rider effect.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Triangle-oriented Community Detection considering Node Features and Network Topology

Gao¹,

Liang²,

Yuan³

et al. 2022

Preprint

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show abstract

“…Based on this idea and thanks to the natural information representation ability of graph structures, graph representation learning has gradually been applied to fraud detection research [4]. Traditional graph representation learning focuses on quantifying the degree of association between nodes, and fraud detection is achieved through subgraph partitioning, which is commonly used (for example by Fraudar [5]), and community discovery algorithms [6,7]. However, traditional graph representation learning exists only for node relationship modeling, and the existence of node features can take full advantage of the disadvantages of the information, graph neural network algorithm well-integrated node relationships, and node features of the two types of information, resulting in a better performance in financial fraud detection [8].…”

Section: Introductionmentioning

confidence: 99%

Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs

Li,

Yang

2023

Systems

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The current fraud risk in digital finance is increasing year by year, and the mainstream solutions rely on the inherent characteristics of users, which makes it difficult to explain fraud behaviors and fraud behavior patterns are less researched. To address these problems, we propose an integrated multiple relational graphs fraud detection model Tri-RGCN-XGBoost, which analyzes the impact of user association patterns on fraud detection by mining the behavioral associations of users. The model builds a heterogeneous information network based on real transaction data, abstracts three types of bipartite graphs (user–device, user–merchant, and user–address), aggregates the information of the user’s neighbor nodes under the three types of behavioral patterns, and integrates the graph convolution classification results under the three behavioral patterns with the XGBoost model to achieve fraudulent user detection with integrated multiple relational graphs. The results show that the performance of this model in fraud identification is significantly improved, especially in reducing the fraudulent user underreporting rate. Further, the behavioral associations that play a key role in fraud user identification are analyzed in conjunction with shape value to provide a reference for fraud pattern mining.

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Localized Community-Based Node Anomalies in Complex Networks

Mukherjee

Kumar

2023

Lecture Notes in Networks and Systems

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eRiskCom: an e-commerce risky community detection platform

Cited by 14 publications

References 60 publications

Triangle-oriented Community Detection considering Node Features and Network Topology

Triangle-oriented Community Detection considering Node Features and Network Topology

Research on Financial Fraud Detection Models Integrating Multiple Relational Graphs

Localized Community-Based Node Anomalies in Complex Networks

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