Interdisciplinary Seminar Series

Title: Metric Forensics: A Multi-Level Approach for Mining Volatile Graphs

Speaker: Tina Eliassi-Rad, Rutgers University

Date: Monday, October 4, 2010 12:00 - 1:00 pm

Location: DIMACS Center, CoRE Bldg, Room 431, Rutgers University, Busch Campus, Piscataway, NJ


Advances in data collection and storage capacity have made it increasingly possible to collect highly volatile graph data for analysis. Existing graph analysis techniques are not appropriate for such data, especially in cases where streaming or near real-time results are required. An example that has drawn significant research interest is the cyber-security domain, where internet communication traces are collected and real-time discovery of events, behaviors, patterns, and anomalies is desired. We propose Metric Forensics, a scalable framework for analysis of volatile graphs. Metric Forensics combines a multi-level "drill down" approach, a collection of user-selected graph metrics, and a collection of analysis techniques. At each successive level, more sophisticated metrics are computed and the graph is viewed at finer temporal resolutions. In this way, Metric Forensics scales to highly volatile graphs by only allocating resources for computationally expensive analysis when an interesting event is discovered at a coarser resolution first. We test Metric Forensics on three real-world graphs: an enterprise IP trace, a trace of legitimate and malicious network traffic from a research institution, and the MIT Reality Mining proximity sensor data. Our largest graph has ~3M vertices and ~32M edges, spanning 4.5 days. The results demonstrate the scalability and capability of Metric Forensics in analyzing volatile graphs; and highlight four novel phenomena in such graphs: elbows, broken correlations, prolonged spikes, and lightweight stars.

Bio: Tina Eliassi-Rad is an Assistant Professor at the Department of Computer Science at Rutgers University. She is also a member of the Rutgers Center for Computational Biomedicine, Imaging, and Modeling (CBIM) and Rutgers Center for Cognitive Science (RuCCS). Until September 2010, Tina was a Member of Technical Staff at Lawrence Livermore National Laboratory. Tina earned her Ph.D. in Computer Sciences (with a minor in Mathematical Statistics) at the University of Wisconsin-Madison in 2001. Broadly speaking, Tina's research interests include machine learning, data mining, and artificial intelligence. Her work has been applied to the World-Wide Web, text corpora, large-scale scientific simulation data, and complex networks. Tina is an action editor for the Data Mining and Knowledge Discovery Journal. She received a US DOE Office of Science Outstanding Mentor Award in 2010. For more details, visit