FraudNE: a Joint Embedding Approach for Fraud Detection

Detecting fraudsters is a meaningful problem for both users and e-commerce platform. Existing graph-based approaches mainly adopt shallow models, which cannot capture the highly non-linear relationship between vertexes in a bipartite graph composed of users and items. To address this issue, in this paper we propose a joint deep structure embedding approach FraudNE for fraud detection that (a) can preserve the highly non-linear structural information of networks, (b) is robust to sparse networks, (c) embeds different types of vertexes jointly in the same latent space. It is worth mentioning that we can detect multiple fraudulent groups without the number of groups as a priori. Compared with baselines, our method achieved significant accuracy improvement.

[1]  Leman Akoglu,et al.  Discovering Opinion Spammer Groups by Network Footprints , 2015, ECML/PKDD.

[2]  Shirui Pan,et al.  Finding the best not the most: regularized loss minimization subgraph selection for graph classification , 2015, Pattern Recognit..

[3]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[4]  Deepak S. Turaga,et al.  A Spectral Framework for Detecting Inconsistency across Multi-source Object Relationships , 2011, 2011 IEEE 11th International Conference on Data Mining.

[5]  Hyun Ah Song,et al.  FRAUDAR: Bounding Graph Fraud in the Face of Camouflage , 2016, KDD.

[6]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[7]  Christos Faloutsos,et al.  Spotting Suspicious Link Behavior with fBox: An Adversarial Perspective , 2014, 2014 IEEE International Conference on Data Mining.

[8]  Philip S. Yu,et al.  Multiple Structure-View Learning for Graph Classification , 2018, IEEE Transactions on Neural Networks and Learning Systems.

[9]  Geoffrey E. Hinton,et al.  Stochastic Neighbor Embedding , 2002, NIPS.

[10]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[11]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[12]  Li Guo,et al.  CPMF: A collective pairwise matrix factorization model for upcoming event recommendation , 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

[13]  Mikhail Belkin,et al.  Laplacian Eigenmaps for Dimensionality Reduction and Data Representation , 2003, Neural Computation.

[14]  Nitesh V. Chawla,et al.  metapath2vec: Scalable Representation Learning for Heterogeneous Networks , 2017, KDD.

[15]  Karen Spärck Jones.  A statistical interpretation of term specificity and its application in retrieval , 2021, J. Documentation.

[16]  Ee-Peng Lim,et al.  Detecting product review spammers using rating behaviors , 2010, CIKM.

[17]  Li Guo,et al.  Social Recommendation with an Essential Preference Space , 2018, AAAI.

[18]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[19]  Bing Liu,et al.  Opinion spam and analysis , 2008, WSDM '08.

[20]  Christos Faloutsos,et al.  M-Zoom: Fast Dense-Block Detection in Tensors with Quality Guarantees , 2016, ECML/PKDD.

[21]  Charu C. Aggarwal,et al.  An embedding approach to anomaly detection , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).

[22]  Chun Wang,et al.  MGAE: Marginalized Graph Autoencoder for Graph Clustering , 2017, CIKM.

[23]  Christos Faloutsos,et al.  EigenSpokes: Surprising Patterns and Scalable Community Chipping in Large Graphs , 2010, PAKDD.

[24]  Christos Faloutsos,et al.  HoloScope: Topology-and-Spike Aware Fraud Detection , 2017, CIKM.

[25]  Fei Wang,et al.  Structural Deep Embedding for Hyper-Networks , 2017, AAAI.

[26]  Claire Cardie,et al.  Finding Deceptive Opinion Spam by Any Stretch of the Imagination , 2011, ACL.

[27]  Christos Faloutsos,et al.  Inferring Strange Behavior from Connectivity Pattern in Social Networks , 2014, PAKDD.

[28]  Chengqi Zhang,et al.  Tri-Party Deep Network Representation , 2016, IJCAI.

[29]  Charu C. Aggarwal,et al.  Heterogeneous Network Embedding via Deep Architectures , 2015, KDD.

[30]  Christos Faloutsos,et al.  D-Cube: Dense-Block Detection in Terabyte-Scale Tensors , 2017, WSDM.

[31]  Chengqi Zhang,et al.  Multi-graph-view Learning for Graph Classification , 2014, 2014 IEEE International Conference on Data Mining.

[32]  Chuan Zhou,et al.  Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation , 2017, AAAI.

[33]  Li Guo,et al.  On the Minimum Differentially Resolving Set Problem for Diffusion Source Inference in Networks , 2016, AAAI.

[34]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[35]  Yi Yang,et al.  Learning to Identify Review Spam , 2011, IJCAI.

[36]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[37]  Arjun Mukherjee,et al.  Spotting fake reviewer groups in consumer reviews , 2012, WWW.