A social network model driven by events and interests

• An agent-based model for social network modeling is proposed.
• The model merges the attribute-based and structure-based dynamical processes.
• The evolution of the model is driven by events and interests.
• The model is capable of reproducing many realistic network statistics and patterns.

A proper understanding of how complex networks grow is important to get insights into the network structure, make predictions of future growth, and enable simulation of large networks. In this paper, we focus on social networks and try to understand, capture and predict dynamic behaviors on social networks. How social networks evolve, i.e. how individuals create and deactivate social ties? It is interesting for several areas such as marketing, web search and recommendation.We propose an agent-based model in which agents represent individuals and social networks evolve driven by events and interests. The model, which we call EIM (Event-Interest Model), incorporates an intuitive idea: individuals begin to interact when participating in the same event, and social ties are formed or reinforced between two individuals if they have similar interests and would like to link to each other.Our model matches better to realistic network structure in terms of a number of statistical properties and critical patterns compared with some recent models for social networks, which suggests that both events and interests may play an important role in shaping the evolution of social networks.