Research on the Influence of Information Iterative Propagation on Complex Network Structure

被引：1

作者：

Qian, Yinuo ^{[1
]}

Nian, Fuzhong ^{[1
]}

Wang, Zheming ^{[1
]}

Yao, Yabing ^{[1
]}

机构：

[1] Lanzhou Univ Technol, Sch Comp & Commun, Lanzhou, Gansu, Peoples R China

来源：

BIG DATA | 2024年

基金：

中国国家自然科学基金;

关键词：

complex network; information iterative propagation; link prediction; propagation weighted network; COMMUNITY STRUCTURE; LINK PREDICTION; CENTRALITY; ALGORITHM;

D O I：

10.1089/big.2023.0016

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Dynamic propagation will affect the change of network structure. Different networks are affected by the iterative propagation of information to different degrees. The iterative propagation of information in the network changes the connection strength of the chain edge between nodes. Most studies on temporal networks build networks based on time characteristics, and the iterative propagation of information in the network can also reflect the time characteristics of network evolution. The change of network structure is a macromanifestation of time characteristics, whereas the dynamics in the network is a micromanifestation of time characteristics. How to concretely visualize the change of network structure influenced by the characteristics of propagation dynamics has become the focus of this article. The appearance of chain edge is the micro change of network structure, and the division of community is the macro change of network structure. Based on this, the node participation is proposed to quantify the influence of different users on the information propagation in the network, and it is simulated in different types of networks. By analyzing the iterative propagation of information, the weighted network of different networks based on the iterative propagation of information is constructed. Finally, the chain edge and community division in the network are analyzed to achieve the purpose of quantifying the influence of network propagation on complex network structure.

引用

页数：14

共 44 条

[1] Prediction of higher-order links using global vectors and Hasse diagrams [J].

Anjan, Kalpnil ;

Andreopoulos, Wiliam ;

Potika, Katerina .

2021 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2021, :4802-4811

[2] VeSoNet: Traffic-Aware Content Caching for Vehicular Social Networks Using Deep Reinforcement Learning [J].

Aung, Nyothiri ;

Dhelim, Sahraoui ;

Chen, Liming ;

Lakas, Abderrahmane ;

Zhang, Wenyin ;

Ning, Huansheng ;

Chaib, Souleyman ;

Kechadi, Mohand Tahar .

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2023, 24 (08) :8638-8649

[3] Network neuroscience [J].

Bassett, Danielle S. ;

Sporns, Olaf .

NATURE NEUROSCIENCE, 2017, 20 (03) :353-364

[4] Extracting significant signal of news consumption from social networks: the case of Twitter in Italian political elections [J].

Becatti, Carolina ;

Caldarelli, Guido ;

Lambiotte, Renaud ;

Saracco, Fabio .

PALGRAVE COMMUNICATIONS, 2019, 5 (1)

[5]

Cai QX, 2019, INT J UNCONV COMPUT, V14, P449

[6] Higher-order Link Prediction Using Triangle Embeddings [J].

Chavan, Neeraj ;

Potika, Katerina .

2020 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2020, :4535-4544

[7] Learning on Attribute-Missing Graphs [J].

Chen, Xu ;

Chen, Siheng ;

Yao, Jiangchao ;

Zheng, Huangjie ;

Zhang, Ya ;

Tsang, Ivor W. .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2022, 44 (02) :740-757

[8] Community detection in complex networks using extremal optimization [J].

Duch, J ;

Arenas, A .

PHYSICAL REVIEW E, 2005, 72 (02)

[9] CENTRALITY IN SOCIAL NETWORKS CONCEPTUAL CLARIFICATION [J].

FREEMAN, LC .

SOCIAL NETWORKS, 1979, 1 (03) :215-239

[10] Community structure in jazz [J].

Gleiser, PM ;

Danon, L .

ADVANCES IN COMPLEX SYSTEMS, 2003, 6 (04) :565-573

← 1 2 3 4 5 →