Identifying Influential People Based on Interaction Strength
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초록
Extraction of influential people from their respective domains has attained the attention of scholasticcommunity during current epoch. This study introduces an innovative interaction strength metric forretrieval of the most influential users in the online social network. The interactive strength is measured bythree factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, wedesign a novel algorithm called IPRank that considers the communications from perspectives of followers andfollowees in order to mine and rank the most influential people based on proposed interaction strengthmetric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blognetwork. The comparative analysis validates that IPRank discovered high ranked people in terms ofinteraction strength. While the prior algorithm placed some low influenced people at high rank. Theproposed model uncovers influential people due to inclusion of a novel interaction strength metric thatimproves results significantly in contrast with prior algorithm.
Abstract
Extraction of influential people from their respective domains has attained the attention of scholasticcommunity during current epoch. This study introduces an innovative interaction strength metric forretrieval of the most influential users in the online social network. The interactive strength is measured bythree factors, namely re-tweet strength, commencing intensity and mentioning density. In this article, wedesign a novel algorithm called IPRank that considers the communications from perspectives of followers andfollowees in order to mine and rank the most influential people based on proposed interaction strengthmetric. We conducted extensive experiments to evaluate the strength and rank of each user in the micro-blognetwork. The comparative analysis validates that IPRank discovered high ranked people in terms ofinteraction strength. While the prior algorithm placed some low influenced people at high rank. Theproposed model uncovers influential people due to inclusion of a novel interaction strength metric thatimproves results significantly in contrast with prior algorithm.
참고문헌 (26)
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