Discovering shared interests using graph analysis
Communications of the ACM - Special issue on internetworking
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Mining knowledge-sharing sites for viral marketing
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Handbook of data mining and knowledge discovery
Handbook of data mining and knowledge discovery
Intelligent data analysis
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
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Network Data Mining builds network linkages (network models) between myriads of individual data items and utilizes special algorithms that aid visualization of ‘emergent' patterns and trends in the linkage. It complements conventional and statistically based data mining methods. Statistical approaches typically flag, alert or alarm instances or events that could represent anomalous behavior or irregularities because of a match with pre-defined patterns or rules. They serve as ‘exception detection' methods where the rules or definitions of what might constitute an exception are able to be known and specified ahead of time. Many problems are suited to this approach. Many problems however, especially those of a more complex nature, are not well suited. The rules or definitions simply cannot be specified; there are no known suspicious transactions. This paper presents a human-centered network data mining methodology. A case study from the area of security illustrates the application of the methodology and corresponding data mining techniques. The paper argues that for many problems, a ‘discovery' phase in the investigative process based on visualization and human cognition is a logical precedent to, and complement of, more automated ‘exception detection' phases.