The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Communications of the ACM
Understanding belief propagation and its generalizations
Exploring artificial intelligence in the new millennium
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Combating web spam with trustrank
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Netprobe: a fast and scalable system for fraud detection in online auction networks
Proceedings of the 16th international conference on World Wide Web
Spectrum based fraud detection in social networks
Proceedings of the 17th ACM conference on Computer and communications security
Bazaar: strengthening user reputations in online marketplaces
Proceedings of the 8th USENIX conference on Networked systems design and implementation
A novel two-stage phased modeling framework for early fraud detection in online auctions
Expert Systems with Applications: An International Journal
Reputation inflation detection in a Chinese C2C market
Electronic Commerce Research and Applications
OddBall: spotting anomalies in weighted graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Managing and mining large graphs: patterns and algorithms
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Journal of Theoretical and Applied Electronic Commerce Research
Non-negative residual matrix factorization: problem definition, fast solutions, and applications
Statistical Analysis and Data Mining
EigenBot: foiling spamming botnets with matrix algebra
Proceedings of the ACM SIGKDD Workshop on Intelligence and Security Informatics
An effective early fraud detection method for online auctions
Electronic Commerce Research and Applications
Fraud detection in web transactions
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Methodology for fraud detection in electronic transactions
Proceedings of the 18th Brazilian symposium on Multimedia and the web
Top-N recommendation through belief propagation
Proceedings of the 21st ACM international conference on Information and knowledge management
Generating realistic online auction data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
A probability-based trust prediction model using trust-message passing
Proceedings of the 22nd international conference on World Wide Web companion
On the hardness of evading combinations of linear classifiers
Proceedings of the 2013 ACM workshop on Artificial intelligence and security
Data stream mining for predicting software build outcomes using source code metrics
Information and Software Technology
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
Fast generalized subset scan for anomalous pattern detection
The Journal of Machine Learning Research
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Online auctions have gained immense popularity by creating an accessible environment for exchanging goods at reasonable prices. Not surprisingly, malevolent auction users try to abuse them by cheating others. In this paper we propose a novel method, 2-Level Fraud Spotting (2LFS), to model the techniques that fraudsters typically use to carry out fraudulent activities, and to detect fraudsters preemptively. Our key contributions are: (a) we mine user level features (e.g., number of transactions, average price of goods exchanged, etc.) to get an initial belief for spotting fraudsters, (b) we introduce network level features which capture the interactions between different users, and (c) we show how to combine both these features using a Belief Propagation algorithm over a Markov Random Field, and use it to detect suspicious patterns (e.g., unnaturally close-nit groups of people that trade mainly among themselves). Our algorithm scales linearly with the number of graph edges. Moreover, we illustrate the effectiveness of our algorithm on a real dataset collected from a large online auction site.