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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2015, Volume 9284, pp. 267 - 282
Conference Proceeding
Decision Support Systems, ISSN 0167-9236, 07/2015, Volume 75, Issue C, pp. 38 - 48
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2019, Volume 11051, pp. 87 - 104
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2018, Volume 10937, pp. 196 - 208
Conference Proceeding
ACM Transactions on Knowledge Discovery from Data (TKDD), ISSN 1556-4681, 07/2016, Volume 10, Issue 4, pp. 1 - 33
Journal Article
Data Mining and Knowledge Discovery, ISSN 1384-5810, 10/2009, Volume 19, Issue 2, pp. 194 - 209
We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capable of... 
Simulation and modeling | Graph generators | Model validation and analysis | COMPUTER SCIENCE, INFORMATION SYSTEMS | COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE | Computer simulation | Usage | Computer-generated environments
Journal Article
Proceedings of the 21th ACM SIGKDD International Conference on knowledge discovery and data mining, 08/2015, Volume 2015-, pp. 985 - 994
Online reviews capture the testimonials of "real" people and help shape the decisions of other consumers. Due to the financial gains associated with positive... 
scalable algorithms | metadata | semi-supervised learning | opinion spam | heterogenous networks
Conference Proceeding
Management Science, ISSN 0025-1909, 9/2017, Volume 63, Issue 9, pp. 3090 - 3110
We study the impact of network information for social security fraud detection. In a social security system, companies have to pay taxes to the government.... 
fraud detection | bipartite graphs | network analysis | fraud propagation | guilt by association | Fraud propagation | Guilt by association | Fraud detection | Network analysis | Bipartite graphs | OPERATIONS RESEARCH & MANAGEMENT SCIENCE | MANAGEMENT | FRAMEWORK | CLASSIFICATION | ALGORITHMS | COMPLEX NETWORKS | Usage | Algorithms | Fraud | Investigations
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2019, Volume 11051, pp. 139 - 156
Conference Proceeding
2010, Lecture Notes in Computer Science, ISBN 3642136710, Volume 6119, Issue 2
Given a large, weighted graph, how can we find anomalies? Which rules should be violated, before we label a node as an anomaly? We propose the oddball... 
Information Systems Applications (incl.Internet) | Computer Science | Information Storage and Retrieval | Database Management | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem Complexity | Computer Communication Networks
Book Chapter
ACM Transactions on Knowledge Discovery from Data (TKDD), ISSN 1556-4681, 03/2018, Volume 12, Issue 2, pp. 1 - 40
Given a network with node attributes, how can we identify communities and spot anomalies? How can we characterize, describe, or summarize the network in a... 
summarization | ego networks | Attributed graphs | social circles | community extraction | visual analytics | human-in-the-loop | interaction design | network measures | anomaly mining | COMPUTER SCIENCE, INFORMATION SYSTEMS | NETWORKS | COMPUTER SCIENCE, SOFTWARE ENGINEERING
Journal Article
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2014, Volume 8506, pp. 147 - 159
Conference Proceeding
Future Internet, ISSN 1999-5903, 03/2017, Volume 9, Issue 1, p. 6
The critical process of hiring has relatively recently been ported to the cloud. Specifically, the automated systems responsible for completing the recruitment... 
Hiring | Help wanted ads | Automation | Fraud | Computer security | Recruitment | natural language processing | employment scam | data mining | fraud detection | online recruitment | job scam | machine learning | dataset
Journal Article
Information Sciences, ISSN 0020-0255, 05/2015, Volume 302, pp. 70 - 82
Community question answering (CQA) has become a new paradigm for seeking and sharing information. In CQA sites, users can ask and answer questions, and provide... 
CQA | Question and answer | Voting correlation | Voting prediction | COMPUTER SCIENCE, INFORMATION SYSTEMS | Algorithms | Correlation | Voting | Communities | Feedback | Correlation analysis | Data sets | Gain | Recognition
Journal Article
Proceedings of the 22nd ACM SIGSAC Conference on computer and communications security, ISSN 1543-7221, 10/2015, Volume 2015-, pp. 1696 - 1697
How do anomalies, fraud, and spam effect our models of normal user behavior? How can we modify our models to catch fraudsters? In this tutorial we will answer... 
fraud detection | recommendation systems | user behavior modeling | anomalous behavior | outlier detection | Recommendation systems | User behavior modeling | Anomalous behavior | Outlier detection | Fraud detection
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2017, Volume 10235, pp. 220 - 232
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, 2019, Volume 11051, pp. 122 - 138
Conference Proceeding
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