Complex Graph Modeling, Mining and Search

Monday, November 7, 2011 - 9:36am


Monday, November 14, 2011
3:30 – 4:30 PM
Computer Science Conference Room, Harold Frank Hall Rm. 1132

SPEAKER: Xifeng Yan
Assistant Professor, UCSB Computer Science

Title: Complex Graph Modeling, Mining and Search


Real-life networks are complex, not only having topological structures,
but also containing heterogeneous contents and attributes. There is a
need to develop a general graph information system to change the state
of the art of graph analytics. Specifically, the mixture of structures
and contents raises two challenges. First, new types of graph search and
mining operations, such as graph aggregation, graph association, and
graph pattern mining, need to be defined. Second, when graphs become
complex and large, most of the existing graph mining algorithms cannot
scale well. In this talk, I will give an overview of our effort that
aims to address these challenges. Then I will demonstrate new kinds of
graph patterns identified by our research: proximity pattern,
correlation pattern, iceberg pattern, etc. In the second half of the
talk, I will introduce our recent work on distributed graph processing
that could facilitate searching and mining of large-scale networks. I
will also discuss our newest progress on genome sequence assembly,
neural networks, and collaborative network modeling.


Xifeng Yan is an assistant professor at the University of California at
Santa Barbara. He holds the Venkatesh Narayanamurti Chair in Computer
Science. He received his Ph.D. degree in Computer Science from the
University of Illinois at Urbana-Champaign in 2006. He was a research
staff member at the IBM T. J. Watson Research Center between 2006 and
2008. He has been working on modeling, managing, and mining large-scale
graphs in bioinformatics, social networks, the Web, and computer
systems. His works were extensively referenced, with over 4,500
citations per Google Scholar. He received NSF CAREER Award. For more
information, please visit