Discovering bottlenecks entities in multi-layer social networks

Abstract

In network theory, the interaction among entities in the real-life system is modeled in the form of networks called a social network. Sometimes there exist multiple types of communication among entities, which is further designed into multiple networks called multi-layer networks. Recently measuring the structural properties for multi-layer networks has attracted significant interest among researchers and centrality measures is one of them. In this paper, an algorithm for calculating the bottleneck centrality for entities in multi-layer networks is proposed. Bottleneck centrality helps to find such nodes, which appear in more than n/4 times in the path while evaluating the shortest path among nodes of the network. Such shortest paths between nodes are calculated by considering all layers of the multilayer networks. Nodes having a high value of bottleneck centrality are the key connecter nodes of the network. The proposed algorithm is demonstrated on two multi-layer network datasets. The results are compared with the aggregated systems of the same datasets and few other centrality measures to evaluate the efficiency and effectiveness of the working of the algorithm.

Keywords:

• Bottleneck centrality
• Multiplex network
• Social Network
• Multiple layers
• Centrality measures
• Betweenness centrality
• Trend lines
• Multiplex centrality measure