A self-organization method for discovering communities in a distributed network

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.