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Collective Classification in Network Data
Open accessAbstract
Many real‐world applications produce networked data such as the worldwide web (hypertext documents connected through hyperlinks), social networks (such as people connected by friendship links), communication networks (computers connected through communication links), and biological networks (such as protein interaction networks). A recent focus in machine‐learning research has been to extend traditional machine‐learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real‐world data.
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“The Cora data set contains 2708 machine learning publications grouped into seven classes.”
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“Each paper in Pubmed is represented by a term frequency-inverse document frequency (TF-IDF) vector.”
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“Exact inference in the collective classification problem is NP-hard, and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [37].”
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“Exact inference in the collective classification problem is NP-hard, and solutions have focused on the use of approximate inference algorithm which may not be guaranteed to converge [37].”
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Zhou, F., Cao, C., Zhang, K., Trajcevski, G., Zhong, T., & Ji, G. (2019) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3357384.3358106 · Cited by 244
Adversarial Attacks and Defenses on Graphs
Jin, W., Li, Y., Xu, H., Wang, Y., Ji, S., Aggarwal, C. C., & Tang, J. (2021) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3447556.3447566 · Cited by 267
Sparse Graph Attention Networks
Ye, Y., & Ji, S. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3072345 · Cited by 240
UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks
Huang, J. Y., & Yang, J. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/353 · Cited by 286
Generative and Contrastive Self-Supervised Learning for Graph Anomaly Detection
Zheng, Y., Jin, M., Liu, Y., Chi, L., Phan, K. T., & Chen, Y. P. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3119326 · Cited by 233
Metadata
- Authors
- Sen, P., Namata, G., Bilgic, M., Getoor, L., Gallagher, B., & Eliassi‐Rad, T.
- Affiliations
- University of Maryland, College Park (US)
- Venue
- AI Magazine
- Publisher
- Wiley
- Published
- 2008-09
- Volume
- 29
- Issue
- 3
- Pages
- 93-106
- ISSN
- 0738-4602 (ISSN-L)
- 2371-9621
- OpenAlex
- W2153959628
- Cited by
- 3,293
- References
- 73
- Citations by year
- 2026: 186 · 2025: 416 · 2024: 409 · 2023: 393 · 2022: 346 · 2021: 437 · 2020: 325 · 2019: 205 · 2018: 100 · 2017: 69 · 2016: 52 · 2015: 67 · 2014: 62 · 2013: 59 · 2012: 50
- FWCI
- 29.90
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- AI Magazine (publishedVersion)
- University Libraries (University of Maryland) (submittedVersion)
- Funders
- National Science Foundation (0308030)
- U.S. Department of Energy (DE‐AC52‐07NA27344)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Keywords
- collective classification, network data, node classification, inference algorithms, social networks, biological networks, communication networks, protein-protein interaction network, algorithm comparison
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