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Fractal information dissemination and clustering evolution on social hypernetwork
Li Luo1, Fuzhong Nian1, Yuanlin Cui1
1School of Computer and Communication, Lanzhou University of Technology, Lanzhou 730050, China.
Chaos (Woodbury, N.Y.)
|September 19, 2024
Summary
Complex systems are better understood using higher-order networks that model group interactions. Key groups with overlapping structures significantly influence information dissemination and network evolution.
Area of Science:
- Complex systems analysis
- Network science
- Social network analysis
Background:
- Classical networks have limitations in modeling complex systems due to their inability to capture higher-order interactions.
- Higher-order networks offer enhanced modeling capacities for group interactions, aiding in understanding and predicting network dynamics.
Purpose of the Study:
- To construct a social hypernetwork incorporating group structures and analyze community overlap.
- To define metrics like group cognitive disparity and credibility to study information dissemination and network evolution.
Main Methods:
- Constructed a social hypernetwork by analyzing community overlapping structures and iterative relationships.
- Defined and utilized group cognitive disparity, group credibility, group cohesion index, and hyperedge strength.
- Analyzed the relationship between information dissemination and network evolution within the hypernetwork.
Main Results:
- Groups can alter connected networks through information propagation, with users forming highly connected groups.
- Propagation networks with high clustering coefficients facilitate fractal information dissemination, driving fractal group evolution.
- Overlapping structures within "key groups" play a significant role in group network propagation.
Conclusions:
- Higher-order network analysis provides a more comprehensive understanding of complex systems and social network dynamics.
- Group behavior and overlapping structures are critical factors in information spread and network evolution.
- The study highlights the emergent properties of group formation and fractal evolution in social networks.
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