 |
|
|
|
|
|
|
|
|
|
|
|
|
 |
|
|||||||||||
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
 |
|
|
 |
 |
|
||||||||||
 |
|
|||||||||||
 |
|
|||||||||||
 |
 |
|
||||||||||
| Seminar Schedule (Seminar Organizer: Prof. Ziv Bar-Joseph) | |
Machine Learning Lunchtime Chats
|
Fall 2004 Seminar
Date: November 12, 2004 Time: 1:00 PM - 2:00 PM Location: 4623 Wean Hall Speaker: Deepayan Chakrabarti PhD Candidate Title: Tools for Large Graph Mining Abstract: Graphs show up in a surprisingly diverse set of disciplines, ranging from computer networks to sociology, biology, ecology and many more. How do such "normal" graphs look like? How can we spot abnormal subgraphs within them? Which nodes/edges are "suspicious?" How does a virus spread over a graph? Answering these questions is vital for outlier detection (such as terrorist cells, money laundering rings), forecasting, simulations (how well will a new protocol work on a realistic computer network?), immunization campaigns and many other applications. We attempt to answer these questions in two parts. First, we investigate recurring patterns in real-world graphs, to gain a deeper understanding of their structure. This leads to the development of the R-MAT model of graph generation for creating synthetic but "realistic" graphs, which match many of the patterns found in real-world graphs, including power-law and lognormal degree distributions, small diameter and "community" effects. The second part of our work is targeted at applications: what patterns/properties of a graph are important for solving specific problems? Here, we investigate the propagation behavior of a computer virus over a network, and find a simple formula for the epidemic threshold (beyond which any viral outbreak might become an epidemic). We also develop a scalable, parameter-free method for finding groups of "similar" nodes in a graph, corresponding to homogeneous regions (or CrossAssociations) in the binary adjacency matrix of the graph. This can help navigate the structure of the graph, and find un-obvious patterns.Our proposed work involves extending these ideas, and applying these principles to other areas.