Co-author Relationship Prediction in Heterogeneous Bibliographic Networks
Abstract
The problem of predicting links or interactions between objects in a network, is an important task in network analysis. Along this line, link prediction between co-authors in a co-author network is a frequently studied problem. In most of these studies, authors are considered in a homogeneous network, i.e., only one type of objects (author type) and one type of links (co-authorship) exist in the network. However, in a real bibliographic network, there are multiple types of objects (e.g., venues, topics, papers) and multiple types of links among these objects. In this paper, we study the problem of co-author relationship prediction in the heterogeneous bibliographic network, and a new methodology called PathPredict, i.e., meta path-based relationship prediction model, is proposed to solve this problem. First, meta path-based topological features are systematically extracted from the network. Then, a supervised model is used to learn the best weights associated with different topological features in deciding the co-author relationships. We present experiments on a real bibliographic network, the DBLP network, which show that metapath-based heterogeneous topological features can generate more accurate prediction results as compared to homogeneous topological features. In addition, the level of significance of each topological feature can be learned from the model, which is helpful in understanding the mechanism behind the relationship building.
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Developments in heterogeneous relationship predictions introduced and formalized a new framework that takes into account network heterogeneity by defining type-specific node-edge paths or meta-paths.
“Developments in heterogeneous (54) relationship predictions (55) introduced and formalized a new framework that takes into account network heterogeneity by defining typespecific node-edge paths or meta-paths (56).”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [55]
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Artificial Intelligence for Drug Discovery: Are We There Yet?
Hasselgren, C., & Oprea, T. I. (2024) · Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-040323-040828 · Cited by 236 · see citing works
“Developments in heterogeneous (54) relationship predictions (55) introduced and formalized a new framework that takes into account network heterogeneity by defining typespecific node-edge paths or meta-paths (56).”
BackgroundHimmelstein, D. S., & Baranzini, S. E. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004259 · Cited by 172 · see citing works
Autophagy dark genes: Can we find them with machine learning?
Ranjbar, M., Yang, J. J., Kumar, P., Byrd, D. R., Bearer, E. L., & Oprea, T. I. (2023) · Natural Sciences · doi:10.1002/ntls.20220067 · Cited by 7 · see citing works
- Authors
- Sun, Y., Barber, R., Gupta, M., Aggarwal, C. C., & Han, J.
- Affiliations
- University of Illinois Urbana-Champaign (US)
- Venue
- 2011 International Conference on Advances in Social Networks Analysis and Mining
- Publisher
- IEEE
- Published
- 2011-07
- Pages
- 121-128
- OpenAlex
- W2109480754
- Cited by
- 445
- References
- 15
- Citations by year
- 2026: 3 · 2025: 10 · 2024: 14 · 2023: 17 · 2022: 21 · 2021: 43 · 2020: 42 · 2019: 37 · 2018: 36 · 2017: 68 · 2016: 42 · 2015: 35 · 2014: 38 · 2013: 19 · 2012: 18
- FWCI
- 21.36
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- heterogeneous networks, bibliographic networks, meta-paths, topological properties
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