EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation

Zhang, Jiabao; Liu, Shenghua; Yu, Wenjian; Feng, Wenjie; Cheng, Xueqi

doi:10.1007/978-3-030-16145-3_39

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…FRAUDAR [28] proposes a subgraph density of suspicion metric for improving the accuracy of detecting both disguised and non-disguised frauds and finding fraudsters in the case of disguised or hijacked accounts. EigenPulse [29] proposes a row-enhanced matrix with a sliding window to model the flow graph and found a density surge subgraph in the singular spectrum-based flow graph. MonLAD [30] proposes a streaming graph-based model that computes residual statistical features and tries to explain such streaming-based behaviors.…”

Section: Money Laundering Identificationmentioning

confidence: 99%

Structural entropy minimization combining graph representation for money laundering identification

Wang,

et al. 2024

Int. J. Mach. Learn. & Cyber.

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Money laundering identification (MLI) is a challenging task for financial AI research and application due to its massive transaction volume, label sparseness, and label bias. Most of the existing MLI methods focus on individual-level abnormal behavior while neglecting the community factor that money laundering is a collaborative group crime. Furthermore, the massive volume of transactions and the issue of label shifting also impede the application of supervised or semi-supervised models. To this end, this paper proposes an efficient community-oriented algorithm, namely SEGE, to identify money laundering based on structural entropy minimization (SEM) with graph embedding in an unsupervised approach. Experiments on both a private real-world money laundering network and a public synthetic dataset show that our SEGE algorithm derives prominent performance and outperforms the parameterized learning-based graph representation methods. Moreover, we find that there are pervasive sub-communities in the real-world money laundering network. Based on our local algorithm, we propose a real combat strategy against the money laundering group, in which when we have several scattered suspicious accounts in the transaction network, we are able to retrieve the whole money laundering group by the union of sub-communities with both high precision and high recall rates.

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Section: Money Laundering Identificationmentioning

confidence: 99%

Structural entropy minimization combining graph representation for money laundering identification

Wang,

et al. 2024

Int. J. Mach. Learn. & Cyber.

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“…• Although with no application to AML, but mostly for the discovery of fake reviews, FRAUDAR [15] detects dense subgraphs in bipartite graphs optimizing a suspiciousness function. EigenPulse [16] deals with a similar problem, but on streaming graphs.…”

Section: A Detecting Known Patternsmentioning

confidence: 99%

Anomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications

Dumitrescu

Bălțoiu

Budulan³

2022

IEEE Access

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Our aim in this paper is to detect bank clients involved in suspicious activities related to money laundering, using the graph of transactions of the bank. Although we have a labeled real dataset, our target is not only to obtain relevant results on it, but also on random graphs in which typical anomaly patterns have been injected. So, we want simultaneously adequacy to the real data and robustness. Our method is based on designing new features; the most important are those resulting from the reduced egonet, which is the subgraph that remains from an egonet after eliminating the nodes connected with a single edge to the center; another feature is built by appealing to random walks and serves as indicator of circular flows. Our features are added to usual egonet features and a general anomaly detection algorithm, in our case Isolation Forest, serves to detect the anomalies. Experiments on the real data and a comprehensive set of synthetic data show that our approach is adequate, robust and better than some previous methods.

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“…Our work is closely related to areas like anomaly detection on graphs [16][17][18][19][20][21][22][23] and streams [24][25][26][27][28][29][30][31][32], and streaming algorithms [33][34][35][36][37]. Higher-order sketches are discussed in [37], however, they are restricted to count-sketches and non-graph settings.…”

Section: Related Workmentioning

confidence: 99%

Sketch-Based Anomaly Detection in Streaming Graphs

Bhatia¹,

Wadhwa²,

Kawaguchi³

et al. 2021

Preprint

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Given a stream of graph edges from a dynamic graph, how can we assign anomaly scores to edges and subgraphs in an online manner, for the purpose of detecting unusual behavior, using constant time and memory? For example, in intrusion detection, existing work seeks to detect either anomalous edges or anomalous subgraphs, but not both. In this paper, we first extend the count-min sketch data structure to a higher-order sketch. This higher-order sketch has the useful property of preserving the dense subgraph structure (dense subgraphs in the input turn into dense submatrices in the data structure). We then propose four online algorithms that utilize this enhanced data structure, which (a) detect both edge and graph anomalies; (b) process each edge and graph in constant memory and constant update time per newly arriving edge, and; (c) outperform state-of-the-art baselines on four real-world datasets. Our method is the first streaming approach that incorporates dense subgraph search to detect graph anomalies in constant memory and time.

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EigenPulse: Detecting Surges in Large Streaming Graphs with Row Augmentation

Cited by 8 publications

References 13 publications

Structural entropy minimization combining graph representation for money laundering identification

Structural entropy minimization combining graph representation for money laundering identification

Anomaly Detection in Graphs of Bank Transactions for Anti Money Laundering Applications

Sketch-Based Anomaly Detection in Streaming Graphs

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