Health Care Fraud Detection with Community Detection Algorithms

Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.

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Community Detection for Mobile Money Fraud Detection

2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) ◽

10.1109/snams52053.2020.9336578 ◽

2020 ◽

Author(s):

Safa El Ayeb ◽

Baptiste Hemery ◽

Fabrice Jeanne ◽

Estelle Cherrier

Keyword(s):

Community Detection ◽

Fraud Detection ◽

Mobile Money

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Community Detection in Multiplex Networks

ACM Computing Surveys ◽

10.1145/3444688 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-35

Author(s):

Matteo Magnani ◽

Obaida Hanteer ◽

Roberto Interdonato ◽

Luca Rossi ◽

Andrea Tagarelli

Keyword(s):

Community Detection ◽

Experimental Evaluation ◽

Network Models ◽

Ground Truth ◽

Community Structures ◽

Multiplex Networks ◽

Detection Algorithms ◽

Multiplex Network ◽

The Right ◽

Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

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Deep autoencoder-based community detection in complex networks with particle swarm optimization and continuation algorithms

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-201342 ◽

2021 ◽

pp. 1-17

Author(s):

Mohammed Al-Andoli ◽

Wooi Ping Cheah ◽

Shing Chiang Tan

Keyword(s):

Particle Swarm Optimization ◽

Complex Networks ◽

Learning Community ◽

Community Detection ◽

Particle Swarm ◽

Premature Convergence ◽

Swarm Optimization ◽

Detection Algorithms ◽

Real World Datasets ◽

The Cost

Detecting communities is an important multidisciplinary research discipline and is considered vital to understand the structure of complex networks. Deep autoencoders have been successfully proposed to solve the problem of community detection. However, existing models in the literature are trained based on gradient descent optimization with the backpropagation algorithm, which is known to converge to local minima and prove inefficient, especially in big data scenarios. To tackle these drawbacks, this work proposed a novel deep autoencoder with Particle Swarm Optimization (PSO) and continuation algorithms to reveal community structures in complex networks. The PSO and continuation algorithms were utilized to avoid the local minimum and premature convergence, and to reduce overall training execution time. Two objective functions were also employed in the proposed model: minimizing the cost function of the autoencoder, and maximizing the modularity function, which refers to the quality of the detected communities. This work also proposed other methods to work in the absence of continuation, and to enable premature convergence. Extensive empirical experiments on 11 publically-available real-world datasets demonstrated that the proposed method is effective and promising for deriving communities in complex networks, as well as outperforming state-of-the-art deep learning community detection algorithms.

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