Multi-objective based unbiased community identification in dynamic social networks

A network is a topological arrangement of its two basic elements, nodes and edges. Networks in the real world are not static. They tend to evolve with time, causing the set of nodes and edges to alter as well. They consist of several hidden bits of data whose analysis have drawn significant research interest. Identifying groups of similar nodes or edges helps in gaining knowledge about their interaction patterns. These groups are known as communities, which can be disjoint or overlapping. The dynamic nature of the network also impact its current community structure and makes it difficult to keep track of them. The paper presents a multi-objective optimization approach for identifying community structure in a dynamic network. A network is considered as a series of events generated over time, where each event is a new edge introduced at a time. The proposed algorithm uses three objective functions that are inspired from network properties. The community of a node corresponding to an input edge is updated by an algorithm based on its newness. The algorithm uses the Pareto front principle to identify the optimal community. The algorithm is evaluated over 12 datasets and compared to 10 state-of-the-art algorithms. It shows superior performance on real and connected datasets and also performs well for disconnected datasets. The algorithm is evaluated using both accuracy and quality metrics, with the quality metrics slightly outweighing the accuracy metrics.