Managing and mining large graphs: patterns and algorithms
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
GigaTensor: scaling tensor analysis up by 100 times - algorithms and discoveries
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
ParCube: sparse parallelizable tensor decompositions
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
MultiAspectForensics: mining large heterogeneous networks using tensor
International Journal of Web Engineering and Technology
Network Anomaly Detection Using Co-clustering
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
CopyCatch: stopping group attacks by spotting lockstep behavior in social networks
Proceedings of the 22nd international conference on World Wide Web
Interesting pattern mining in multi-relational data
Data Mining and Knowledge Discovery
| Hi-index | 0.00 |
Modern applications such as web knowledge base, network traffic monitoring and online social networks have made available an unprecedented amount of network data with rich types of interactions carrying multiple attributes, for instance, port number and time tick in the case of network traffic. The design of algorithms to leverage this structured relationship with the power of computing to assist researchers and practitioners for better understanding, exploration and navigation of this space of information has become a challenging, albeit rewarding, topic in social network analysis and data mining. The constantly growing scale and enriching genres of network data always demand higher levels of efficiency, robustness and generalizability where existing approaches with successes on small, homogeneous network data are likely to fall short. We introduce MultiAspectForensics, a handy tool to automatically detect and visualize novel sub graph patterns within a local community of nodes in a heterogenous network, such as a set of vertices that form a dense bipartite graph whose edges share exactly the same set of attributes. We apply the proposed method on three data sets from distinct application domains, present empirical results and discuss insights derived from these patterns discovered. Our algorithm, built on scalable tensor analysis procedures, captures spectral properties of network data and reveals informative signals for subsequent domain-specific study and investigation, such as suspicious port-scanning activities in the scenario of cyber-security monitoring.