Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/14836
Title: Graph Energy Based Centrality Measure to Identify Influential Nodes in Social Networks
Authors: Kamath S.S.
Mahadevi S.
Issue Date: 2019
Citation: 2019 IEEE 5th International Conference for Convergence in Technology, I2CT 2019 , Vol. , , p. -
Abstract: 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.
URI: https://doi.org/10.1109/I2CT45611.2019.9033792
http://idr.nitk.ac.in/jspui/handle/123456789/14836
Appears in Collections:2. Conference Papers

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