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Human behavior is intricately shaped by social influences that arise from interactions with others in diverse contexts. These influences not only mold beliefs and attitudes but also drive the regulation of behaviors through both direct communication and observational learning. The study of these processes falls within the domain of social psychology, which seeks to understand how individuals are affected by and affect those around them.Mechanisms of Social InfluenceDirect social influence...
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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

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Published on: May 31, 2019

Identifying communities by influence dynamics in social networks.

Angel Stanoev1, Daniel Smilkov, Ljupco Kocarev

  • 1Macedonian Academy for Sciences and Arts, Skopje, Macedonia. astanoev@cs.manu.edu.mk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|December 21, 2011
PubMed
Summary

This study introduces a novel community detection algorithm for social networks. It models dynamic social interactions to identify leaders, evolving communities, and overlapping community structures.

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Area of Science:

  • Network Science
  • Social Network Analysis
  • Computational Social Science

Background:

  • Communities are dynamic entities, constantly evolving, merging, and disappearing.
  • Traditional community detection algorithms often overlook the dynamical processes inherent in network evolution.
  • Effective community detection requires integrating network topology with the processes driving community formation and change.

Purpose of the Study:

  • To develop a community detection algorithm that incorporates dynamical social processes.
  • To focus specifically on social networks and model dynamic social interactions.
  • To identify leaders, evolving communities, and overlapping community structures within social networks.

Main Methods:

  • The algorithm combines network structure with processes supporting community creation and evolution.
  • It models dynamic social interactions within social networks.
  • Nodes are assigned membership vectors to naturally support overlapping communities.

Main Results:

  • The algorithm successfully identifies leaders and communities formed around them.
  • It effectively handles overlapping communities by quantifying node membership.
  • It can identify nodes acting as bridges between communities.

Conclusions:

  • The developed algorithm offers a dynamic approach to community detection in social networks.
  • It provides insights into leadership, community evolution, and inter-community relationships.
  • The methodology has potential applications beyond social network analysis.