Name
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 rind web communities of network structure without any domain information. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CEC.2008.4630930 | IEEE Congress on Evolutionary Computation |
Keywords | Field | DocType |
algorithm design and analysis,extremal optimization,information retrieval,network architecture,domain knowledge,search engines,service oriented architecture,optimization,evolutionary computation,internet,testing,web search engine,particle swarm optimization | Web search engine,Particle swarm optimization,Data mining,Algorithm design,Extremal optimization,Computer science,Local optimum,Network architecture,Network simulation,Artificial intelligence,Web community,Machine learning | Conference |
Volume | Issue | ISSN |
null | 03 | null |
ISBN | Citations | PageRank |
978-1-4244-1823-7 | 16 | 0.79 |
References | Authors | |
2 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Duan Xiaodong | 1 | 85 | 16.18 |
| Cunrui Wang | 2 | 16 | 0.79 |
| Xiangdong Liu | 3 | 568 | 20.32 |
| Yanping Lin | 4 | 16 | 0.79 |