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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.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
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“Each paper in Pubmed is represented by a term frequency-inverse document frequency (TF-IDF) vector.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
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“The Pubmed data set contains 19717 diabetes-related publications.”
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“Each paper in Cora and Citeseer is represented by a one-hot vector indicating the presence or absence of a word from a dictionary.”
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“The Citeseer data set contains 3327 scientific papers grouped into six classes.”
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In the citing paper
“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].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [37]
In the citing paper
“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].”
From DeepWalk: online learning of social representations (2014) · cites this paper as [37]
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Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
Li, S., Kim, D., & Wang, Q. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-86520-7_28 · Cited by 30
Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks
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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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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