Influential Node Detection in Social Networks for Real-World Applications

Abstract

Influential node detection is a crucial task in real-world networks, such as social networks, biological networks, or information networks. It helps us to understand network dynamics as well as identify important participants. A commonly used method for detecting important nodes involves the utilisation of community detection, which seeks to divide the network into clusters of nodes that reflect high levels of connectivity. The Overlapping Community Detection approach named as Internal-External overlapping Community Detection method has been proposed to find different communities. We have introduced a new factor namely Influence Detection Factor that is used to determine influential node of a network. State transition of every node occurred in a time-varying network. State transition based on the influential node can be useful for tracking different frames of every influential node with time. Dynamic Social network analysis in disease spreading is an emerging field of research. Disease Network can be considered a real-world application in various contexts. We have considered COVID-19 India data as a case study. Each node and edge is depicted as a province/district and the number of COVID-19 cases, in the network respectively. Edge Weight is calculated using our proposed metric. We have used different centrality measures, such as degree centrality, closeness centrality, betweenness centrality, etc., to compare our metrics over real-world datasets. The observations obtained through our proposed method demonstrate a significant correlation with the real-time occurrences. Experimental results of our proposed methodology demonstrate better results in comparison to other existing approaches.