A New Algorithm for Detecting Local Community Based on Random Walk
Abstract: This paper presents
one new algorithm for local community discovery. It employs a new vertex
selection strategy which considers not only the boundary structure of candidate
local community but also the probability which the investigated vertex will
return to the candidate local community. A local random walk is adopted to
compute this return probability which does not require the global information.
We choose four algorithms for comparison which are the best ones existed by
far. For better evaluation, the datasets include not only the computer
generated graphs in standard benchmark but also the real-world networks which
are classical ones in global community discovery. The experimental results show
our algorithm outperforms the other ones on the computer generated graphs. The
performance of our algorithm is approximately the same with the algorithm
proposed by Luo, Wang and Promislow on real-world networks.
Author: Yueping Li, Weikun
Zheng
Journal Code: jptkomputergg140111
