CS595D - Graph Mining, Weekly Seminar

Abstract: Graph mining and network analytics is critical to a variety of application domains, ranging from community detection in social networks, malicious program analysis in computer security, to searches for functional modules in biological pathways and structural analysis in chemical compounds. There is an emerging need to systematically investigate the modeling, managing, and mining of large-scale graphs and networks in bioinformatics, social networks, and computer systems. In this seminar, we are going to discuss the state-of-the-art research results and identify potential topics for graduate research in graph mining.

Enrollment Code: 78469,  Instructor: Prof. Ambuj Singh and Prof. Xifeng Yan , Email: ambuj at cs.ucsb.edu and xyan at cs.ucsb.edu

Time: Tuesday 1:00-3:00pm, Location: CS Conference Room (Harold Frank Hall Rm. 1132)

Week/Topics Sources Slides
Week 1 (Jan 6)
Week 2 (Jan 13)
Graph Clustering
* Stijn van Dongen. A cluster algorithm for graphs, pdf
* Stijn van Dongen. A stochastic uncoupling process for graphs, pdf
* Graph Clustering by Flow Simulation, website
Week 3 (Jan 20)
Link Analysis, Relational Learning
* Collective Classification in Network Data,  Prithviraj Sen, Galileo Mark Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, Tina Eliassi-Rad, Technical Report Petko,
Week 4 (Jan 27)
Graph Patterns
* SIGKDD'08 Graph Mining tutorial slides and related papers Sayan,
Week 5 (Feb 3)
Approximate Graph Patterns (biological networks)
* Sharan, R., Suthram, S., Kelley, R. M., Kuhn, T., McCuine, S., Uetz, P., Sittler, T., Karp, R. M., and Ideker, T. Cover Article: Conserved patterns of protein interaction in multiple species, Proc Natl Acad Sci U S A. 8:102(6) 1974-79 (2005), pdf
* Kelley, B. P., Sharan, R., Karp, R., Sittler, E. T., Root, D. E., Stockwell, B. R., and Ideker, T. Conserved pathways within bacteria and yeast as revealed by global protein network alignment. Proc Natl Acad Sci U S A 100, 11394-9 (2003),  pdf
* Jason Flannick, Antal Novak, Chuong B. Do, Balaji S. Srinivasan and Serafim Batzoglou. Automatic Parameter Learning for Multiple Network Alignment, (RECOMB'08) pdf
Week 6 (Feb 10)
Graph Summarization
and Visualization
* SIGMOD 2008, Saket Navlakha, Rajeev Rastogi, Nisheeth Shrivastava: Graph summarization with bounded error. 419-432. pdf
* SIGMOD 2008, Yuanyuan Tian, Richard A. Hankins, Jignesh M. Patel: Efficient aggregation for graph summarization. 567-580, pdf
* Graph Visualization (TBD)
Week 7 (Feb 17)
Graph Dynamics
* WWW 2003,  R. Kumar, J. Novak, P. Raghavan, and A. Tomkins: On the bursty evolution of blogspace, pdf
* S. Dorogovtsev and J. Mendes: Evolution of networks,  pdf
* D. Chakrabarti: R. Kumar and A. Tomkins: Evolutionary Clustering, pdf
open discussion
Week 8 (Feb 24)
Graph Kernel
* SIGKDD'08 Graph Mining tutorial slides and related papers Nan,
Week 9 (Mar 3)
Social Network
* CIDR 2009, Social Systems: Can We Do More Than Just Poke Friends?
* Social network applications and data sources
* Social network data mining
open discussion
Week 10 (Mar 10)
Graph Modeling and Statistics
(social networks)
* WWW'08 Graph Modeling tutorial slides and related papers, (Leskovec and Faloutsos)
Part I and Part II

Students may register for one unit in CS595D; to receive credit, they must sign in and can miss no more than two sessions. This seminar is open to the public as well, due to the wide interest that graph mining holds for various disciplines.