Conference Papers
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Item Graph Energy Based Centrality Measure to Identify Influential Nodes in Social Networks(Institute of Electrical and Electronics Engineers Inc., 2019) Kamath, S.S.; Mahadevi, S.One of the measures to analyze complex network is vertex centrality; it can reveal existing network patterns. It helps us in understanding networks. One of the measures to analyze complex network is vertex centrality, and it can reveal existing network patterns. It helps us in understanding networks and their components by analyzing their structural properties. The social network is one of the complex networks which is composed of nodes and relationships. It is growing very vastly due to the addition of new nodes every day. All nodes are not equally important in such a vast network hence, identifying influential nodes becomes a practical problem. Centrality measures were introduced to quantify the importance of nodes in networks. The various criterion is used to select critical nodes in the network. Therefore, different centrality measures like Betweenness Centrality, Degree Centrality, Closeness Centrality, and other well-known centrality measures are used to identify essential nodes. We have proposed an algorithm to compute a centrality using graph energy called Energy-Based-Centrality-Measure (EBCM) in this paper. It identifies the central nodes based on a graph invariant called graph energy. EBCM gives a better understanding of the current network by analyzing the impact of node deletion on graph connectivity and thereby helps us in achieving a better network understanding ability and maintenance. © 2019 IEEE.Item Analysis of Kapferer Mine Network using Graph Energy Ranking(Institute of Electrical and Electronics Engineers Inc., 2019) Mahadevi, S.; Kamath, S.S.Vertex centrality is one of the procedures to evaluate complex networks, and it can disclose current patterns of networks. By evaluating their structural characteristics, it enables us to understand networks and their elements. One of the complex networks of nodes and interactions is the social network. It is increasing very greatly every day owing to the addition of fresh nodes. In such a vast network, therefore, not all nodes are equally essential, identifying influential nodes becomes a practical issue. To quantify the significance of nodes in networks, centrality measures were implemented. The multiple criteria are used to select critical nodes in the network. Various centrality measures such as Betweenness Centrality, Degree Centrality, Closeness Centrality, and some well-known centrality measures are therefore used to define vital nodes. In this article, we suggested a centrality to rank the nodes using a graph invariant called graph energy named as Graph-Energy-Ranking (GER). GER provides a better knowledge of the current network by evaluating the effect of node deletion on graph connectivity and thus enables us to better understand and maintain the network. In the current paper GER is applied on well-known social network called Kapferer mine network and results have been discussed. © 2019 IEEE.