Faculty Publications
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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 Graph energy ranking for scale-free networks using Barabasi-Albert model(Institute of Electrical and Electronics Engineers Inc., 2019) Mahadevi, S.; Kamath, S.S.A social network is a vast collection of actors and interactions. It forms one of the complex networks. There are various types of social networks such as acquaintance networks, online social networks, covert networks, citation networks, and collaboration networks, etc. Most of these real-world networks are scale-free, and they follow a power-law distribution. Each of these networks has nodes which have various roles to play, and all nodes are not equally important. Hence we need to rank them based on their importance. In this paper, we propose an algorithm named Graph Energy Ranking (GER) to rank the nodes of scale-free networks built using the Barabasi-Albert model. GER analyses the impact of node deletion on the underlying network and therefore gives a better understanding of the network features. Study of ranking done by existing centrality measures versus GER is performed to observe the similarity in the ranking process. ©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.Item Study of novel COVID-19 data using graph energy centrality: a soft computing approach(Inderscience Publishers, 2022) Mahadevi, S.; Kamath, S.S.; Shetty D, D.P.The propagation of the new pandemic COVID-19 is more likely linked to human social relations and activities. A social network can be used to describe these human relationships and activities. Understanding the dynamic properties of disease dissemination through diverse social networks is critical for effective and efficient infection prevention and control. With the frequent emergence and spread of infectious diseases and their impact on large areas of the population, there is growing interest in modelling these complex epidemic behaviour. Such an approach could provide a stronger decision-making method to tackle and control disease. In this paper, a transmission network is developed using the South Korean data, and the study of the network is carried out using graph energy centrality. This measure of centrality allows us to recognise the primary cause of the spread of the virus within the established network by ranking the nodes of the network based on graph energy. The identified primary cause can then be isolated, which can prevent further spread of infection. We have also considered the Novel Corona Virus 2019 Dataset from Johns Hopkins University to analyse epidemiological data around the world. © © 2022 Inderscience Enterprises Ltd.Item Graph energy centrality: a new centrality measurement based on graph energy to analyse social networks(Inderscience Publishers, 2022) Mahadevi, S.; Kamath, S.S.; Shetty D, P.D.Critical node identification, one of the key issues in social network analysis, is addressed in this article with the development of a new centrality metric termed graph energy centrality (GEC). The fundamental idea underlying this GEC measure is to give each vertex a centrality value based on the graph energy that results from vertex elimination. We show that the GEC of each vertex is asymptotically equal to two for cycle graphs and exactly equal to two for complete graphs. We further demonstrate that star graphs can be ranked using only two GEC values, whereas path graphs can be ranked using a maximum of ⌈n+12 ⌉ values. The proposed algorithm takes O(n3) time complexity to rank all vertices; hence an optimised algorithm is also being proposed considering only a few classes of graphs. The proposed algorithm ranks the nodes based on the collaborative measure of eigenvalues. © 2022 Inderscience Enterprises Ltd.. All rights reserved.