A complex network is an explicit model for a real-world system such as technological networks, social networks, business networks, and biological networks. The social network is an Internet-based media for socially relevant activities like stay connected with families and friends, colleagues, and customers, for socializing, business, or both. The key nodes, usually called central nodes, capable of measuring the performance of various social network applications. Identifying influencing nodes is primary research for any network analysis research. Degree centrality, a locally computed metric, is simple to compute but not persuasive. The global metrics like betweenness centrality and PageRank are only useful for the systems with a simple structure, but incur a high computational cost with the addition of layers. This paper proposes a novel metric m-PageRank for ranking nodes in a multi-layer complex network. The m-PageRank is an advancement of PageRank. It integrates the existence of the rank of each layer from where the connection connects. The proposed metric was validated through simulations performed over various multilayer networks. The result shows that m-PageRank computes the rank of each node accurately. We observe that the comparison with state-of-the-art metrics demonstrated the suitability of the proposed metric.
Efficient algorithms for estimating loss of information in a complex network: Applications to intentional risk analysis
