Characterizing the arising of hot topics as a transformation from modular to nested

Chen, Baitong; Kang, Yujie

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https://hdl.handle.net/2142/126218 Copy

Description

Title

Characterizing the arising of hot topics as a transformation from modular to nested

Author(s)

• Chen, Baitong
• Kang, Yujie

Issue Date

2025-03-11

Keyword(s)

• Topic evolution
• Modularity
• Nestedness
• Bipartite network

Abstract

With the rapid development of science and technology, analyzing the evolution of academic topics has become an important task in information science research. Among related work of topic evolution research, little is known about how hot topics arises and the corresponding network dynamics. This paper reveals the dynamic characteristics of topic evolution by viewing the scientific communities as mutualistic systems, where authors and their research interests resembles the mutualistic structure in the natural communities of ecological systems. The experiment, based on data from the Artificial Intelligence (AI) domain in the Scopus database, constructs author-keyword bipartite networks and analyzes the changes in hot topics and interaction patterns over the years using the modularity and nestedness metrics. Results show that the arising of hot topics is characterized as a transforming process from modular to nested networks, reflecting trends in topic aggregation and the interaction between authors and topics. This paper not only enriches the methods for analyzing scientific topic evolution, but also provides new perspectives for understanding the complex dynamics of academic fields.

Publisher

iSchools

Series/Report Name or Number

iConference 2025 Proceedings

Type of Resource

Other

Genre of Resource

Conference Poster

Language

eng

Handle URL

https://hdl.handle.net/2142/126218

Copyright and License Information

Copyright 2025 is held by Baitong Chen and Yujie Kang. Copyright permissions, when appropriate, must be obtained directly from the authors.

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