Title
Extracting and ranking viral communities using seeds and content similarity
Abstract
We study the community extraction problem within the context of networks of blogs and forums. When starting from a small set of known seed nodes, we argue that the use of content information (beyond explicit link information) plays an essential role in the identification of the relevant community. Our approach lends itself to a new and insightful ranking scheme for members of the extracted community and an efficient algorithm for inflating/deflating the extracted community. Using a considerably large commercial data set of blog and forum sites, we provide experimental evidence to demonstrate the utility, efficiency, and stability of our methods.
Year
DOI
Venue
2008
10.1145/1379092.1379121
Hypertext 1999
Keywords
Field
DocType
ranking viral community,insightful ranking scheme,small set,explicit link information,content information,experimental evidence,essential role,content similarity,efficient algorithm,community extraction problem,relevant community,forum site,ranking,extraction,similarity,community
Data mining,World Wide Web,Information retrieval,Ranking,Computer science,Small set
Conference
Citations 
PageRank 
References 
3
0.40
22
Authors
3
Name
Order
Citations
PageRank
Hyun Chul Lee120515.50
Allan Borodin22947658.84
Leslie Goldsmith330.40