Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach

TPS: A Topological Potential Scheme to Predict Influential Network Nodes for Intelligent Communication in Social Networks

“…For example, Zareie et al [11] introduce two influential node ranking algorithms that use the diversity of the neighbors of each node to obtain its ranking value. Kumar & Panda [12] propose a coreness-based method to find influential nodes by voting. They also compare the performance of their method with some existing popular methods.…”

A Re-Ranking Algorithm for Identifying Influential Nodes in Complex Networks

“…The details of VoteRank, please refer to [44]. NCVoterank [46] is similar to VoteRank, but in voting Phase, each node u gets a score by the following equation…”

“…Hu et al [45] found that any nodes' global influence can be measured purely from its local network environment without the knowledge of the entire network, which motivated by an efficient algorithm with constant time complexity on the influence maximization problem. Kumar et al [46] proposed a coreness based VoteRank called NCVoteRank to find influential nodes by taking neighbors' coreness into consideration for the voting process. To select a set of influential nodes in networks, a new method named EnRenew was proposed by Guo et al [47].…”

“…The impressive results show that our re-ranking algorithm can significantly improve the performance of original node ranking methods. In addition, we also compare the performance of improved versions with benchmark methods such as HC greedy [36], VoteRank [44], EnRenew [47], Improved K-shell [48] and NCVoteRank [46]. Compared with the best result of these methods, the performance of PageRank_INF (PageRank after re-ranking) improved by 2.9%, 1.4%, 1.5%, 3.6%, 3.2%, and 3.4% in final affected scale on BA, Jazz, PGP, Sex, USAir and Router network, respectively.…”

Modeling information diffusion in online social networks using a modified forest-fire model

“…Social networks like Twitter play an essential role in the analysis of the spread of information. Modeling information diffusion on these networks has many applications like finding trending topics (Mashiach and Sharma 2020), finding influential users (Kumar and Panda 2020), devising marketing strategies, identifying opinion leaders Kimura et al 2013), and many more. In the past, information diffusion models like susceptible-infected-recovered (SIR), susceptible-exposed-infectedrecovered (SEIR), and other similar epidemic models have been used and Cai et al (2012).…”