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

Yu, En-Yu; Fu, Yan; Tang, Qing; Zhao, Jiayin; Chen, Duanbing

doi:10.1109/access.2020.3038791

Cited by 20 publications

(15 citation statements)

References 63 publications

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“…where k j1 , k j2 , ..., k j k i are degrees of node i's neighbors. The goal of H-index is to find a maximum integer x such that there must be at least x neighbors with degrees ≥ x RINF [27] is a critical nodes mining algorithm that can reorder node ranking algorithms such as Degree, k-shell, PageRank, H-index and IMGNN. With the help of a special iterative selection strategy, the overlap of influence among nodes selected by initial algorithms can be effectively weakens and the performance of node ranking algorithms can be improved.…”

Section: Benchmark Methodsmentioning

confidence: 99%

“…The experimental results on one synthetic and five real networks show that, compared with PageRank, H-index, Degree and Kshell, IMGNN has the smallest proportion of initial propagation nodes under different infection probabilities when the final infection scale is greater than 80%. In addition, Also, as a non-iterative selection algorithm, IMGNN can also be reordered by RINF algorithm [27] to further improve its performance. And the reordered version of IMGNN outperforms VoteRank, EnRenew, Improved Kshell and NCVoteRank in most cases.…”

Section: Introductionmentioning

confidence: 99%

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Identifying critical nodes in complex networks via graph convolutional networks

Wang²,

et al. 2020

Knowledge-Based Systems

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106

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Section: Benchmark Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identifying critical nodes in complex networks via graph convolutional networks

Wang²,

et al. 2020

Knowledge-Based Systems

Self Cite

106

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“…x = S 1 .pop(0); ( 16) S = S ∪ x; (17) else (18) For u in S 1 do (19) gain(u) = Spread(S ∪ {u}) − Spread(S);…”

Section: Oel Algorithmmentioning

confidence: 99%

An Influence Maximization Algorithm for Dynamic Social Networks Based on Effective Links

Zhang

Bai

et al. 2022

Entropy

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The connection between users in social networks can be maintained for a certain period of time, and the static network structure formed provides the basic conditions for various kinds of research, especially for discovering customer groups that can generate great influence, which is important for product promotion, epidemic prevention and control, and public opinion supervision, etc. However, the computational process of influence maximization ignores the timeliness of interaction behaviors among users, the screened target users cannot diffuse information well, and the time complexity of relying on greedy rules to handle the influence maximization problem is high. Therefore, this paper analyzes the influence of the interaction between nodes in dynamic social networks on information dissemination, extends the classical independent cascade model to a dynamic social network dissemination model based on effective links, and proposes a two-stage influence maximization solution algorithm (Outdegree Effective Link—OEL) based on node degree and effective links to enhance the efficiency of problem solving. In order to verify the effectiveness of the algorithm, five typical influence maximization methods are compared and analyzed on four real data sets. The results show that the OEL algorithm has good performance in propagation range and running time.

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“…In a recent work [25] , structural similarity of nodes has been considered and original transition matrix has been replaced by similarity matrix to incorporate the fact that the transition probability between nodes and their neighbors is not same. Based on node ranking methods, Yu et al [26] introduced a novel re-ranking algorithm through information spread function to determine a set of influential nodes for complex networks. Yang et al [27] studied how to identify influential spreaders in complex networks based on node-local centrality and network embedding.…”

Section: Introductionmentioning

confidence: 99%