Title :
A self-organization method for discovering communities in a distributed network
Author :
Xiaolong Guo ; Jiajin Huang
Author_Institution :
Int. WIC Inst., Beijing Univ. of Technol., Beijing, China
Abstract :
In recent years, the detection of network community structures has been adopted to address the distributed resource optimization issue in distributed networks. This paper presents an autonomy-oriented distributed search strategy to tackle it. The strategy is based on the ideas of self-organization and positive feedback from the methodology of Autonomy-Oriented Computing (AOC). The strategy uses bio-inspired autonomous agents which can use their edges to distinguish the network communities. Agents are equipped with one behavior (move) and three selections (best selection, better selection and random selection). At every moment, agents probabilistically choose a behavior to perform. Experimental results indicate that the strategy has a positive influence on system performance.
Keywords :
distributed algorithms; multi-agent systems; optimisation; AOC; autonomy oriented computing; autonomy oriented distributed search strategy; discovering communities; distributed network; distributed resource optimization; network community structure detection; self-organization method; Autonomous agents; Clustering algorithms; Communities; Educational institutions; Equations; Heuristic algorithms; Partitioning algorithms;
Conference_Titel :
Natural Computation (ICNC), 2013 Ninth International Conference on
Conference_Location :
Shenyang
DOI :
10.1109/ICNC.2013.6817950