Modeling Adaptive Behaviors on Growing Social Networks

Adaptive networks appear in biological and social applications. They combine topological evolution of network with dynamics of the network nodes. Considering the friendship network in a community, members tend to choose those who share the similar interests to be their friends. With the growth of the social network, the interests of a member could change with the interests of their friends, and one member could only maintain a certain number of friends. Moreover, the friendship between two members decays over time when their interests are different enough. In this paper, based on the above assumption, we propose a simple network evolution mechanism to investigate how adaptive behaviors of members could affect the structure of growing social networks. Using computer simulation, we demonstrate the emergent properties on adaptive social networks generated from our proposed mechanism, and compare these properties with those of other three network evolution models: BA model, small world network and random network model. The state changing in the nodes of the network is examined according to adaptive behaviors. Experimental results show that some adaptive behaviors of members can facilitate the formation of a higher-quality community in which members with similar traits are more closely clustered, i.e. "things of one kind come together".