Influence node analysis based on neighborhood influence vote rank method in social network

Abstract

Social Networks are used for various purposes like advertising, product launches, sentiment analysis, opinion mining, and event detection etc. Terrorist targets social network users to spread the terrorism. Influence analysis is used in social networks to find the influence of users and the impact of the messages, mainly for advertising. In this research, the Neighborhood Influence – Vote Rank (NI-VR) method is proposed to analyze the terrorism and social network datasets temporally to find the influence node in Social networks. The Global Terrorism Dataset (GTD) was used to analyze the terrorism activity and temporal analysis on Social Network data to find the influence node. The Neighborhood node influence is measured and considered in the Social Network data to effectively find the influence node. The nodes’ vote score and vote ability were measured to rank the nodes based on influence. The neighborhood influence is measured to update the vote score and vote ability based on influence value. The neighborhood influence method is applied to rank the node has the advantage of analyzing the probability of affected nodes and recover nodes that help to effectively find the influence nodes. The outcomes illustrate that the proposed NI-VR achieved a maximum spread influence of 843 and the existing Greedy method has a higher spread influence of 840 in influence node analysis.