Community detection using agents in complex networks

Abstract

The main purpose of this work is analyzing the complex networks. Mainly, a new community detection algorithm based on using software agents will be introduced. The technical background will be given in details and the performance of the algorithm for different networks will be examined. First, the main concepts in complex networks and the information about usage of the software agents will be introduced. Then, the current methods finding the communities in complex networks will be presented. The community is defined to be the group of nodes which are densely connected within the group but rarely connected to the outside. There have been many algorithm proposed so far to detect the communities in complex networks. However, most of them have some weaknesses. For instance, some algorithms need some prior information about the network to find the communities such as number of communities. There are also some algorithms with higher complexity values. Yet, these algorithms are not feasible or applicable to the real world complex networks. Our algorithm proposes a new algorithm to detect the communities in complex networks and addresses these issues. An algorithm is developed that utilizes software agents for gathering information about the network and uses network modularity to decide on the community among candidates. To test the accuracy of the algorithm, the networks with known community structure like Zachary Karate Club network are used. The large networks such as web sites network that we created by observing a proxy server are also used to test the complexity and scalability of the algorithm. Besides, the performance with a computer generated network is also presented. Finally, it can be said that the algorithm is able to reveal the nearly optimum communities with acceptable complexity.