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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

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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.

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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.