The DOI matches its Crossref record; 72 of the 73 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“The Pubmed data set contains 19717 diabetes-related publications.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“Each paper in Cora and Citeseer is represented by a one-hot vector indicating the presence or absence of a word from a dictionary.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
In the citing paper
“The Citeseer data set contains 3327 scientific papers grouped into six classes.”
From A Comprehensive Survey on Graph Neural Networks (2021) · cites this paper as [117]
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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Statistical mechanics of complex networks
Albert, R., & Barabási, A.-L. (2002) · Reviews of Modern Physics · doi:10.1103/revmodphys.74.47 · Cited by 20,471
Birds of a Feather: Homophily in Social Networks
McPherson, M., Smith-Lovin, L., & Cook, J. M. (2001) · Annual Review of Sociology · doi:10.1146/annurev.soc.27.1.415 · Cited by 18,567
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Advances in Neural Information Processing Systems 14
(2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.001.0001 · Cited by 9,187
Markov Chain Monte Carlo in Practice
Gilks, W. R., Richardson, S. T., & Spiegelhalter, D. J. (1995) · Chapman & Hall/CRC eBooks · doi:10.1201/b14835 · Cited by 7,927
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
Factor graphs and the sum-product algorithm
Kschischang, F. R., Frey, B. J., & Loeliger, H.-A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.910572 · Cited by 6,486
Near Shannon limit error-correcting coding and decoding : Turbo-codes
Berrou, C. (1993) · International Conference on Communications · Cited by 4,264
On the Statistical Analysis of Dirty Pictures
Besag, J. E. (1986) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.2517-6161.1986.tb01412.x · Cited by 4,204
Research works printed but not linked: 7
- Craven M., 1998, Proceedings of the Fifteenth National Conference on Artificial Intelligence
- Lu Q., 2003, Machine Learning, Proceedings of the Twentieth International Conference (ICML 2003)
- Macskassy S., 2007, Proceedings of the Twenty‐Second Conference on Artificial Intelligence
- Neville J., 2000, Learning Statistical Models from Relational Data: Papers from the AAAI Workshop
- Taskar B., 2001, Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence
- Taskar B., 2003, Proceedings of the Seventeenth Annual Neural Information Processing Systems Conference
- Yanover C., 2002, Proceedings of the Sixteenth Annual Neural Information Processing Systems Conference
Cited by
Sorted by citations
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
DeepWalk: online learning of social representations
Perozzi, B., Al-Rfou, R., & Skiena, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623732 · Cited by 10,123
Geometric Deep Learning: Going beyond Euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., & Vandergheynst, P. (2017) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2693418 · Cited by 3,661
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,403
Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning
Li, Q., Han, Z., & Wu, X.-M. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11604 · Cited by 2,872
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
