Title :
Web community detection model using particle swarm optimization
Author :
Xiaodong, Duan ; Cunrui, Wang ; Xiangdong, Liu ; Yanping, Lin
Author_Institution :
Res. Inst. of Nonlinear Inf. Technol., Dalian Nat. Univ., Dalian
Abstract :
Web community detection is one of the important ways to enhance retrieval quality of web search engine. How to design one highly effective algorithm to partition network community with few domain knowledge is the key to network community detection. Traditional algorithms, such as Wu-Huberman algorithm, need priori information to detect community, the Radichi algorithm relies on the triangle number in the network, the extremal optimization algorithm proposed by Duch J. is extremely sensitive to the initial solution, easy to fall into the local optimum. This article proposes a new model based on particle swarm optimization to detect network community, and with different scale network chart, Zachary, Krebs and dolphins network architecture to test the algorithm, the experimental results indicate this model can effectively find web communities of network structure without any domain information.
Keywords :
Internet; information retrieval; knowledge acquisition; particle swarm optimisation; search engines; Extremal optimization algorithm; Radichi algorithm; Web community detection model; Web search engine; Wu-Huberman algorithm; dolphins network architecture; domain knowledge; network community detection; particle swarm optimization; retrieval quality; Algorithm design and analysis; Dolphins; Evolutionary computation; Particle swarm optimization; Partitioning algorithms; Search engines; Service oriented architecture; Testing; Web search;
Conference_Titel :
Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1822-0
Electronic_ISBN :
978-1-4244-1823-7
DOI :
10.1109/CEC.2008.4630930