Ranking influential nodes in complex network using edge weight degree based shell decomposition

Identifying critical nodes in a complex network or ranking the nodes based on their influence over the network has many utilities. As an example, these come in handy while choosing customers for viral marketing of a new product or identifying users whom to block for preventing the spreading of misinformation/rumor among others. With proper network modeling, a large number of real-world problems could be analyzed using such node ranking methods. Ranking the nodes based on their spreading influence in a complex network is an important research problem. Network topology-based techniques play a significant role in various applications that require knowing the central entities in a modeled system. Classical network centralities like degree, betweenness, and closeness followed by core decomposition techniques are applied successfully in ranking nodes. Each of them having certain limitations like purely local measure or computation heavy etc. Due to the inherent nature of the real-world problems/events, when they are modeled as a network, most of them transform into a weighted network. Even then a large number of literature is focused on unweighted networks due to the simplicity of computations and evaluation metrics. Many node ranking methods developed for unweighted networks may be employed to weighted networks but they will not be utilize the extra information available as connection strengths. Recently, many authors have started to extend the node ranking heuristics for the weighted networks. In this paper, we propose an edge weight degree neighborhood (EwDN) based shell decomposition technique to rank the nodes of a weighted network. It considers both node degree and edge weight with a proper balance of importance. The SIR epidemic model has been used as a benchmark for simulating the spreading process. The number of generated shells, number of core-shell nodes, and monotonicity have been computed as performance metrics during the evaluation of the methods. Also, Kendall's rank correlation with the benchmark SIR model for various competing heuristics is compared. A performance comparison with recent relevant methods shows a better outcome with the proposed method on real-world weighted networks.