GeniePath: Graph Neural Networks with Adaptive Receptive Paths

Liu, Ziqi; Chen, Chaochao; Li, Longfei; Zhou, Jun; Li, Xiaolong; Song, Le; Yuan, Qi

doi:10.48550/arxiv.1802.00910

Cited by 17 publications

(18 citation statements)

References 11 publications

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“…The system is designed to uncover fraudsters in the claim stage by classifying accounts or orders as fraudulent or not. We specifically address the problem of fraudster gang detection with the help of several powerful graph learning algorithms including unsupervised Deepwalk [16] and supervised DistRep and GeniePath [17]. The merits, knowledge, and practices we learn from applying graph data are discussed and we show how we apply them on our most popular real-world large-scale e-commerce insurance products.…”

Section: A Challenges In Insurance Fraud Detectionmentioning

confidence: 99%

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InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

Chen

Lin

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

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The insurance industry has been creating innovative products around the emerging online shopping activities. Such ecommerce insurance is designed to protect buyers from potential risks such as impulse purchases and counterfeits. Fraudulent claims towards online insurance typically involve multiple parties such as buyers, sellers, and express companies, and they could lead to heavy financial losses. In order to uncover the relations behind organized fraudsters and detect fraudulent claims, we developed a large-scale insurance fraud detection system, i.e., InfDetect, which provides interfaces for commonly used graphs, standard data processing procedures, and a uniform graph learning platform. InfDetect is able to process big graphs containing up to 100 millions of nodes and billions of edges.In this paper, we investigate different graphs to facilitate fraudster mining, such as a device-sharing graph, a transaction graph, a friendship graph, and a buyer-seller graph. These graphs are fed to a uniform graph learning platform containing supervised and unsupervised graph learning algorithms. Cases on widely applied e-commerce insurance are described to demonstrate the usage and capability of our system. InfDetect has successfully detected thousands of fraudulent claims and saved over tens of thousands of dollars daily.

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Section: A Challenges In Insurance Fraud Detectionmentioning

confidence: 99%

“…Common GNN approaches we use for the fraud detection problem are struct2vec [23] and GeniePath [17]. Struct2vec aggregates neighbors by simply summing them up while GeniePath stacks adaptive path layers for breadth and depth exploration in the graph.…”

Section: Graph Learning Algorithmsmentioning

confidence: 99%

InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

Chen

Lin

et al. 2019

2019 IEEE International Conference on Big Data (Big Data)

Self Cite

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“…To avoid the storage of all states, these methods have present the improved training strategies, such as subgraph training [27]or stochastically asynchronous training [28]. Furthermore, some complex networks architecture can utilize gating unite to control the selection of node neighborhood [29], or design two graph convolution networks with the consideration of the local and global consistency on graph [30] , or adjust the receptive field of node on graph by hyper-parameters [31].…”

Section: Graph Neural Networkmentioning

confidence: 99%

Structure fusion based on graph convolutional networks for semi-supervised classification

Lin,

Wang,

Liao

et al. 2019

Preprint

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Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture construction or the salient graph structure preservation, and ignore the the complete graph structure for semi-supervised classification contribution. To mine the more complete distribution structure from multi-view data with the consideration of the specificity and the commonality, we propose structure fusion based on graph convolutional networks (SF-GCN) for improving the performance of semi-supervised classification. SF-GCN can not only retain the special characteristic of each view data by spectral embedding, but also capture the common style of multi-view data by distance metric between multi-graph structures. Suppose the linear relationship between multigraph structures, we can construct the optimization function of structure fusion model by balancing the specificity loss and the commonality loss. By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as adjacent matrix to input graph convolutional networks for semi-supervised classification. Experiments demonstrate that the performance of SF-GCN outperforms that of the state of the arts on three challenging datasets, which are Cora,Citeseer and Pubmed in citation networks.

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“…Earlier works [1,6] use GCNs with constant filter size for the node-based classification task and show the superiority of GCN but do not address the problem of heterogeneity of the graph. In [7], a method is proposed that determines a receptive path for each node rather than a field for performing the convolutions for representation learning. Irrespective of nearest neighbors, the aim is to perform convolutions with selective nodes in the receptive field.…”

Section: Introductionmentioning

confidence: 99%

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Kazi

Shekarforoush

Krishna

et al. 2019

Lecture Notes in Computer Science

115

109

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Geometric deep learning provides a principled and versatile manner for integration of imaging and non-imaging modalities in the medical domain. Graph Convolutional Networks (GCNs) in particular have been explored on a wide variety of problems such as disease prediction, segmentation, and matrix completion by leveraging large, multimodal datasets. In this paper, we introduce a new spectral domain architecture for deep learning on graphs for disease prediction. The novelty lies in defining geometric 'inception modules' which are capable of capturing intra-and inter-graph structural heterogeneity during convolutions. We design filters with different kernel sizes to build our architecture. We show our disease prediction results on two publicly available datasets. Further, we provide insights on the behaviour of regular GCNs and our proposed model under varying input scenarios on simulated data.

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GeniePath: Graph Neural Networks with Adaptive Receptive Paths

Cited by 17 publications

References 11 publications

InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

InfDetect: a Large Scale Graph-based Fraud Detection System for E-Commerce Insurance

Structure fusion based on graph convolutional networks for semi-supervised classification

InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

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