CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics

The prevalence of Internet platforms, such as social media and web pages, enables users to share their opinions and observe each other's actions. Users may show strong advocacy and support for each other's opinions and decisions at one moment, while they may disagree with each other with polarized views later. These dynamic changes of supportive relationships pose challenges to influential entities such as government agencies, firms, politicians, experts and weblebrities, who aim to gain support from and have large influence on the public. To study this dynamics, we consider the Social Networks with Supportive Relationships (SN-SR), whose links represent actively supportive relationships and are disconnected when users disagree with each other. For these influential entities, their reputation and social influence impact each other's evolution over SN-SR, while few works study how to model this co-evolving pattern and how to analyze and predict the dynamics of their influence over networks. In this work, we use the network topology of the SN-SR as an intermediate variable to model and study the interplay between the reputation and influence, and propose an integrated framework called CoRNI to theoretically analyze its impact on social influence. We use simulations on synthetic networks and real Weibo data to validate our proposed model and theoretical analysis. This investigation provides important guidelines for influential entities to adjust their actions and improve their influence over the network.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Signal and Information Processing over Networks
Published
2025-01-01
DOI
https://doi.org/10.1109/tsipn.2025.3572295
Citations
2
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
2.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics

H. Vicky Zhao, Hangjing Zhang, Yixin Dai
2 citations
IEEE Transactions on Signal and Information Processing over Networks
Opinion Dynamics and Social Influence
2.65
article

CoRNI: A Co-Evolutionary Framework Integrating Reputation and Network Structure for Modeling Social Influence Dynamics

H. Vicky Zhao, Hangjing Zhang, Yixin Dai
article en
2 citations

Abstract

The prevalence of Internet platforms, such as social media and web pages, enables users to share their opinions and observe each other's actions. Users may show strong advocacy and support for each other's opinions and decisions at one moment, while they may disagree with each other with polarized views later. These dynamic changes of supportive relationships pose challenges to influential entities such as government agencies, firms, politicians, experts and weblebrities, who aim to gain support from and have large influence on the public. To study this dynamics, we consider the Social Networks with Supportive Relationships (SN-SR), whose links represent actively supportive relationships and are disconnected when users disagree with each other. For these influential entities, their reputation and social influence impact each other's evolution over SN-SR, while few works study how to model this co-evolving pattern and how to analyze and predict the dynamics of their influence over networks. In this work, we use the network topology of the SN-SR as an intermediate variable to model and study the interplay between the reputation and influence, and propose an integrated framework called CoRNI to theoretically analyze its impact on social influence. We use simulations on synthetic networks and real Weibo data to validate our proposed model and theoretical analysis. This investigation provides important guidelines for influential entities to adjust their actions and improve their influence over the network.

IEEE Transactions on Signal and Information Processing over NetworksVol. 11
Tsinghua University (CN)
Reduced inequalities
Openalex Percentile: Top 11%
Opinion Dynamics and Social Influence
2.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.