Finding Influential Nodes by a Fast Marginal Ranking Method

2018 ◽  
pp. 249-261 ◽  
Author(s):  
Yipeng Zhang ◽  
Ping Zhang ◽  
Zhifeng Bao ◽  
Zizhe Xie ◽  
Qizhi Liu ◽  
...  
Keyword(s):  
Ranking Method ◽  
Influential Nodes

scholarly journals Extracting Influential Nodes for Maximization Influence in Social Networks

2021 ◽  
Vol 1818 (1) ◽  
pp. 012177
Author(s):  
Zainab Naseem Attuah ◽  
Firas Sabar Miften ◽  
Evan Abdulkareem Huzan
Keyword(s):  
Social Networks ◽  
Influential Nodes

Influential nodes in planar trusses and meshes using centrality measures

2021 ◽  
Author(s):  
P. Sangeetha ◽  
R. Sundareswaran ◽  
M. Shanmugapriya ◽  
S. Srinidhi ◽  
K. Sowmya
Keyword(s):  
Centrality Measures ◽  
Influential Nodes

CIM: clique-based heuristic for finding influential nodes in multilayer networks

2021 ◽  
Author(s):  
Venkatakrishna Rao. K ◽  
Mahender Katukuri ◽  
Maheswari Jagarapu
Keyword(s):  
Multilayer Networks ◽  
Influential Nodes

NodeRank: Finding influential nodes in social networks based on interests

2021 ◽  
Author(s):  
Mohammed Bahutair ◽  
Zaher Al Aghbari ◽  
Ibrahim Kamel
Keyword(s):  
Social Networks ◽  
Influential Nodes

Sensitivity analysis using Morris: Just screening or an effective ranking method?

2021 ◽  
Vol 455 ◽  
pp. 109648
Author(s):  
Livia Paleari ◽  
Ermes Movedi ◽  
Michele Zoli ◽  
Andrea Burato ◽  
Irene Cecconi ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Ranking Method

scholarly journals Discovering latent node Information by graph attention network

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Weiwei Gu ◽  
Fei Gao ◽  
Xiaodan Lou ◽  
Jiang Zhang
Keyword(s):  
Supervised Learning ◽  
Link Prediction ◽  
Learning Algorithm ◽  
Network Visualization ◽  
Graph Structure ◽  
High Quality ◽  
Node Centrality ◽  
Influential Nodes ◽  
Node Classification ◽  
Highly Cited

AbstractIn this paper, we propose graph attention based network representation (GANR) which utilizes the graph attention architecture and takes graph structure as the supervised learning information. Compared with node classification based representations, GANR can be used to learn representation for any given graph. GANR is not only capable of learning high quality node representations that achieve a competitive performance on link prediction, network visualization and node classification but it can also extract meaningful attention weights that can be applied in node centrality measuring task. GANR can identify the leading venture capital investors, discover highly cited papers and find the most influential nodes in Susceptible Infected Recovered Model. We conclude that link structures in graphs are not limited on predicting linkage itself, it is capable of revealing latent node information in an unsupervised way once a appropriate learning algorithm, like GANR, is provided.

scholarly journals Preference elicitation for a ranking method based on multiple reference profiles

4OR ◽  
2021 ◽  
Author(s):  
Alexandru-Liviu Olteanu ◽  
Khaled Belahcene ◽  
Vincent Mousseau ◽  
Wassila Ouerdane ◽  
Antoine Rolland ◽  
...  
Keyword(s):  
Preference Elicitation ◽  
Ranking Method ◽  
Multiple Reference

scholarly journals Identifying Influential Nodes in Complex Networks Based on Local and Global Methods

2021 ◽  
Vol 1738 ◽  
pp. 012026
Author(s):  
Li Mijia ◽  
Wei Hongquan ◽  
Li Yingle ◽  
Liu Shuxin
Keyword(s):  
Complex Networks ◽  
Influential Nodes
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