Netprobe: a fast and scalable system for fraud detection in online auction networks

By Shashank Pandit, Duen Horng Chau, Samuel Wang, Christos Faloutsos. 2007

469 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

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Citations in the corpus

Attacking Graph-based Classification via Manipulating the Graph Structure (2019, methodology): “Graph-based classification methods have been widely applied to various security problems such as malware detection [14, 59], fraudulent user detection in social networks [10, 25, 36, 67, 68, 72, 74], fake review detection [1, 65], auction fraud detection [47], APT infection detection [46], and attri”
False Information on Web and Social Media: A Survey (2018, background): “Several works show that dense subgraphs produced by coordinated or “lock-step” behavior in the underlying connections of the social (in this case, review) graph are associated with fraudulent behavior [16, 71, 83].”
Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs (2020, background): “For instance, the majority of people tend to connect with people of the opposite gender in dating networks, different amino acid types are more likely to connect in protein structures, fraudsters are more likely to connect to accomplices than to other fraudsters in online purchasing networks [24].”
Anomaly detection in online social networks (2014, methodology): “…Bichler (2011); Cheng and Dickinson (2013)), clandestine organisational structures (e.g. Shetty and Adibi (2005); Krebs (2002); Reid et al. (2005)), and fraudulent and predatory activity (e.g. Phua et al. (2010); Fire et al. (2012); Chau et al. (2006); Akoglu et al. (2013); Pandit et al. (2007)).”
Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods (2021, background): “This is a widely studied task, and past works have noted that non-homophilous structures are present in many such networks [11, 25, 55].”
Opinion Fraud Detection in Online Reviews by Network Effects (2013, background): “Network effects have been exploited in securities fraud (Neville et al. 2005), accounting fraud (McGlohon et al. 2009), and auction fraud (Pandit et al. 2007) detection .”
Digital technologies and artificial intelligence’s present and foreseeable impact on lawyering, judging, policing and law enforcement (2015, methodology): “NetProbe is a tool for detecting fraud at online auction sites (Pandit et al. 2007; Chau et al. 2006).”
Anomaly detection in dynamic networks: a survey (2015, background): “In [60], anomalies (fraudsters) are uncovered in an online auction network by discovering bipartite cores, which are posited to be the interaction behavior between fraudsters and”
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning (2021, background): “Meanwhile, fake users always build relationships with a large number of benign users to increase their reputation and inﬂuence so they can get unexpected beneﬁts, whereas benign users rarely exhibit such activities [28], [29].”
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks (2021, background): “, 2020), and in transaction networks, fraudsters are more likely to connect to accomplices than to other fraudsters (Pandit et al., 2007).”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, background): “2006) online auction fraud X X X 14 (Pandit et al. 2007) online auction fraud X X X”
FRAUDAR: Bounding Graph Fraud in the Face of Camouflage (2016, methodology): “BP has been used for fraud classiﬁ-cation on eBay [21], and fraud detection [1].”
GANG: Detecting Fraudulent Users in Online Social Networks via Guilt-by-Association on Directed Graphs (2017, methodology): “Using undirected social graphs: Some guilt-by-association methods [5], [6], [7], [8], [9], [10], [11], [12], [13] assume symmetric relationships between nodes.”
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks (2013, methodology): “[18] uses belief propagation to find near bipartite cores on the eBay graph, and [24] takes an SVD-like approach to find similar interesting patterns in phone call data.”
Graph based anomaly detection and description: a survey (2014, methodology): “In this part of the survey, we cover a wide range of applications including telecom fraud [Cortes et al., 2002], auction fraud [Pandit et al., 2007], accounting fraud [McGlohon et al., 2009], securities fraud [Neville et al., 2005], opinion spam [Dai et al., 2012, Wang et al., 2011a], trading fraud…”
Graph Representation Learning (2020, background): “The many real-world applications of community detection include uncovering functional modules in genetic interaction networks [Agrawal et al., 2018] and uncovering fraudulent groups of users in financial transaction networks [Pandit et al., 2007].”