Predicting Emerging Interdisciplinary Communities with Functional Attributes of Academic Texts

被引：0
|
作者：
Cao Y. [1 ]
Xiang R. [1 ]
Mao J. [2 ]
Yuan D. [1 ]
机构：
[1] School of Information Management, Central China Normal University, Wuhan
[2] School of Information Management, Wuhan University, Wuhan
来源：
Data Analysis and Knowledge Discovery | / 8卷 / 04期
关键词：
Community Prediction; Emerging Trend; Lexical Function; Machine Learning;
D O I：
10.11925/infotech.2096-3467.2023.1383
中图分类号：
学科分类号：
摘要：
[Objective] This paper explores the diverse characteristics of knowledge network communities to enhance the predicting effectiveness of emerging scientific trends. [Methods] Based on the retrospective growth path of e-Health communities, we proposed a model integrating vocabulary functional attributes to predict emerging trends with diverse features. [Results] In the e-Health field, integrating topic, technical, and other vocabulary functional attribute features can improve the prediction performance of emerging trends. The RF algorithm model, which combines structure, influence, sequence, and attribute features, performed the best. Communities with large vocabulary functional attribute scales, low density, high mediated centrality, and high volatility were more likely to become emerging communities. Sequence features have limited effectiveness in predicting emerging communities, possibly due to the forward-looking impact of emerging communities. [Limitations] The identification results of vocabulary functionality are domain-dependent, and the validity of the conclusions extended to other fields needs further verification. [Conclusions] Fully exploring the fine-grained semantic features of scientific vocabulary can effectively enhance the prediction performance of emerging trends. It provides valuable insights for scientific content evaluation and technology decision support. © 2024 Chinese Academy of Sciences. All rights reserved.
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页码：99 / 111
页数：12
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共 38 条
  • [1] Matsumura N, Matsuo Y, Ohsawa Y, Et al., Discovering Emerging Topics from WWW, Journal of Contingencies and Crisis Management, 10, 2, pp. 73-81, (2002)
  • [2] Zhang S T, Han F., Identifying Emerging Topics in a Technological Domain, Journal of Intelligent & Fuzzy Systems, 31, 4, pp. 2147-2157, (2016)
  • [3] Tu Y N, Seng J L., Indices of Novelty for Emerging Topic Detection[J], Information Processing & Management, 48, 2, pp. 303-325, (2012)
  • [4] Ye Guanghui, Wang Cancan, Li Songye, Recognition and Prediction of Emerging Topics in Interdisciplinary Scientific Research Collaboration Based on SciTS Conference Text, Information Science, 40, 7, pp. 126-135, (2022)
  • [5] Brodka P, Saganowski S, Kazienko P., Group Evolution Discovery in Social Networks, Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining, pp. 247-253, (2011)
  • [6] Yin G S, Chi K, Dong Y X, Et al., An Approach of Community Evolution Based on Gravitational Relationship Refactoring in Dynamic Networks, Physics Letters A, 381, 16, pp. 1349-1355, (2017)
  • [7] Chen J, Zhao H T, Yang X Y, Et al., Community Evolution Prediction Based on Multivariate Feature Sets and Potential Structural Features, Mathematics, 10, 20, (2022)
  • [8] Tajeuna E G, Bouguessa M, Wang S R., Modeling and Predicting Community Structure Changes in Time-Evolving Social Networks, IEEE Transactions on Knowledge and Data Engineering, 31, 6, pp. 1166-1180, (2019)
  • [9] Saganowski S, Brodka P, Koziarski M, Et al., Analysis of Group Evolution Prediction in Complex Networks, PLoS One, 14, 10, (2019)
  • [10] Yu W, Wang W J, Chen X, Et al., Boosting Temporal Community Detection via Modeling Community Evolution Characteristics, Proceedings of the 2019 IEEE International Conference on Big Data and Cloud Computing, pp. 1291-1296, (2019)
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