scholarly journals Identifying Spreading Sources and Influential Nodes of Hot Events on Social Networks

2017 ◽  
pp. 946-954 ◽  
Author(s):  
Nan Zhou ◽  
Xiu-Xiu Zhan ◽  
Qiang Ma ◽  
Song Lin ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
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

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

SIMiner: A Stigmergy-Based Model for Mining Influential Nodes in Dynamic Social Networks

2019 ◽  
Vol 5 (2) ◽  
pp. 223-237 ◽  
Author(s):  
Weihua Li ◽  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Social Networks ◽  
Dynamic Social Networks ◽  
Influential Nodes

Influential Nodes in Social Networks

2020 ◽  
pp. 371-385
Author(s):  
Kousik Das ◽  
Rupkumar Mahapatra ◽  
Sovan Samanta ◽  
Anita Pal
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Structure ◽  
The Social ◽  
Different Types ◽  
Influential Nodes

Social network is the perfect place for connecting people. The social network is a social structure formed by a set of nodes (persons, organizations, etc.) and a set of links (connection between nodes). People feel very comfortable to share news and information through a social network. This chapter measures the influential persons in different types of online and offline social networks.

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

scholarly journals An incremental approach to update influential nodes in dynamic social networks

2020 ◽  
Vol 176 ◽  
pp. 781-790
Author(s):  
Nesrine Hafiene ◽  
Wafa Karoui ◽  
Lotfi Ben Romdhane
Keyword(s):  
Social Networks ◽  
Incremental Approach ◽  
Dynamic Social Networks ◽  
Influential Nodes

Identifying influential nodes in dynamic social networks based on degree-corrected stochastic block model

2016 ◽  
Vol 30 (16) ◽  
pp. 1650092 ◽  
Author(s):  
Tingting Wang ◽  
Weidi Dai ◽  
Pengfei Jiao ◽  
Wenjun Wang
Keyword(s):  
Social Networks ◽  
Real World ◽  
Dynamic Networks ◽  
Dynamic Network ◽  
Community Structures ◽  
Block Model ◽  
Real World Data ◽  
Stochastic Block Model ◽  
Dynamic Social Networks ◽  
Influential Nodes

Many real-world data can be represented as dynamic networks which are the evolutionary networks with timestamps. Analyzing dynamic attributes is important to understanding the structures and functions of these complex networks. Especially, studying the influential nodes is significant to exploring and analyzing networks. In this paper, we propose a method to identify influential nodes in dynamic social networks based on identifying such nodes in the temporal communities which make up the dynamic networks. Firstly, we detect the community structures of all the snapshot networks based on the degree-corrected stochastic block model (DCBM). After getting the community structures, we capture the evolution of every community in the dynamic network by the extended Jaccard’s coefficient which is defined to map communities among all the snapshot networks. Then we obtain the initial influential nodes of the dynamic network and aggregate them based on three widely used centrality metrics. Experiments on real-world and synthetic datasets demonstrate that our method can identify influential nodes in dynamic networks accurately, at the same time, we also find some interesting phenomena and conclusions for those that have been validated in complex network or social science.

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