Fast Algorithms for Querying and Mining Large Graphs

Tong, Hanghang

Title:

Fast Algorithms for Querying and Mining Large Graphs

dc.contributor.author	Tong, Hanghang
dc.contributor.corporatename	Carnegie-Mellon University. Machine Learning Dept.
dc.date.accessioned	2010-03-30T13:42:29Z
dc.date.available	2010-03-30T13:42:29Z
dc.date.issued	2010-03-16
dc.description	Hanghang Tong of Carnegie Mellon University presented a lecture on March 16, 2010 at 2:00 pm in room 1116 E of the Klaus Advanced Computing Building on the Georgia Tech campus.	en
dc.description	Runtime: 58:12 minutes
dc.description.abstract	Graphs appear in a wide range of settings and have posed a wealth of fascinating problems. In this talk, I will present our recent work on (1) querying (e.g., given a social network, how to measure the closeness between two persons? how to track it over time?); and (2) mining (e.g., how to identify abnormal behaviors of computer networks? In the case of virus attacks, which nodes are the best to immunize?) large graphs. For the task of querying, our main finding is that many complex user-specific patterns on large graphs can be answered by means of proximity measurement. In other words, proximity allows us to query large graphs on the atomic levels. Then, I will talk about how to adapt querying tasks to the time evolving graphs. For fast computation of proximity, we developed a family of fast solutions to compute the proximity in several different scenarios. By carefully leveraging some important properties shared by many real graphs (e.g., the block-wise structure, the linear correlation, the skewness of real bipartite graphs, etc), we can often achieve orders of magnitude of speedup with little or no quality loss. For the task of mining, I will talk about immunization and anomaly detection. For immunization, we proposed a near-optimal, fast and scalable algorithm. For anomaly detection, we proposed a family of example-based low-rank matrix approximation methods. The proposed algorithms are provably equal to or better than best known methods in both space and time, with the same accuracy. On real data sets, it is up to 112x faster than the best competitors, for the same accuracy.	en
dc.format.extent	58:12 minutes
dc.identifier.uri	http://hdl.handle.net/1853/32482
dc.language.iso	en_US	en
dc.publisher	Georgia Institute of Technology	en
dc.relation.ispartofseries	Computational Science and Engineering Seminar Series	en_US
dc.subject	Graphs	en
dc.subject	Proximity	en
dc.subject	Immunization	en
dc.subject	Anomaly detection	en
dc.subject	Scalability	en
dc.title	Fast Algorithms for Querying and Mining Large Graphs	en
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
local.relation.ispartofseries	Computational Science and Engineering Seminar Series
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
relation.isSeriesOfPublication	97f53edf-44c2-4e20-855a-72065461737d