GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs

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Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Roughly speaking, there are two categories of graph-based classiﬁcation methods, i.e., collective classiﬁcation [1,9,10, 14,22,23,29,34,39,45,57,62–65,69,71] and graph neural network [6,36,50,66].”
SybilBlind: Detecting Fake Users in Online Social Networks without Manual Labels (2018, background): “One category of Sybil detection approaches leverage the global structure of the social network [39,38,8,26,37,7,33,36,6,14,9,11,30,18,28,29].”
SybilFuse: Combining Local Attributes with Global Structure to Perform Robust Sybil Detection (2018, methodology): “Loopy belief propagation based approaches include SybilBelief [16] and GANG [29].”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “Due to the complexity of anomaly detection and graph data mining [63], [64], [65], [66], [67], in addition to the prior mentioned data-speciﬁc challenges, adopting deep learning techniques for graph anomaly detection also faces a number of challenges from the technical side.”
Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation (2018, methodology): “State-of-the-art collective classiﬁcation methods [2], [5], [6], [11], [13]–[15], [17]–[20], [22]–[29] have three key steps.”