---
title: "Collective Classification in Network Data — Sen 2008"
description: "Many datasets come as graphs: web pages joined by hyperlinks, people joined by communication… 68 references, cited by 118 papers here."
authors: ["Prithviraj Sen", "Galileo Namata", "Mustafa Bilgic", "Lise Getoor", "Brian Gallagher", "Tina Eliassi-Rad"]
year: 2008
venue: "AI Magazine 29(3)"
source: "https://ojs.aaai.org/aimagazine/index.php/aimagazine/article/view/2157"
doi: "10.1609/aimag.v29i3.2157"
licence: "author copy (eliassi.org, tech report version) — read only"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/sen-2008-collective-classification-network-data/"
---
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# Collective Classification in Network Data

**Authors:** [Prithviraj Sen](https://md.research.elamur.ai/free/en/authors/prithviraj-sen/), [Galileo Namata](https://md.research.elamur.ai/free/en/authors/galileo-namata/), Mustafa Bilgic, [Lise Getoor](https://md.research.elamur.ai/free/en/authors/lise-getoor/), Brian Gallagher and Tina Eliassi-Rad · **Year:** 2008 · **Venue:** [AI Magazine 29(3)](https://md.research.elamur.ai/free/en/venues/ai-magazine/) ([AI Magazine 2008](https://md.research.elamur.ai/free/en/venues/ai-magazine/2008/)) · **Publisher:** [Association for the Advancement of Artificial Intelligence](https://md.research.elamur.ai/free/en/publishers/association-for-the-advancement-of-artificial-intelligence/) · **Licence:** author copy (eliassi.org, tech report version) — read only

**Links:** [Publisher's page](https://doi.org/10.1609/aimag.v29i3.2157) · [PDF](http://eliassi.org/papers/ai-mag-tr08.pdf) · [DOI 10.1609/aimag.v29i3.2157](https://doi.org/10.1609/aimag.v29i3.2157) · [OpenAlex](https://openalex.org/W2153959628)

**Citations:** 3,237 citations (OpenAlex, 2026-10-14) · [118](https://md.research.elamur.ai/free/en/papers/sen-2008-collective-classification-network-data/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Many datasets come as graphs: web pages joined by hyperlinks, people joined by communication links, papers joined by citations. Labelling each node in isolation ignores that linked nodes tend to share labels, and the neighbours' labels are often unknown too. The authors survey collective classification, which predicts all unknown labels jointly. It draws on three signals: a node's own attributes, the observed attributes and labels of its neighbours, and the neighbours' still-unknown labels. Exact inference is NP-hard in general, so the report focuses on four approximate methods in two families. The first relies on local conditional classifiers (iterative classification, Gibbs sampling). The second optimizes one global objective (loopy belief propagation, mean-field relaxation labeling). Each method gets pseudo-code, a running webpage example, theoretical motivation and practical advice, and all four are compared on synthetic and real-world networks. The supplied excerpt notes that ICA converges in a few iterations while Gibbs sampling needs many more; the detailed accuracy results sit in the omitted part. The paper remains a standard introduction to node classification in networks.

## Key points
- Covers four inference algorithms: iterative classification (ICA), Gibbs sampling (GS), loopy belief propagation (LBP, also called the sum-product algorithm) and mean-field relaxation labeling (MF).
- Neighbourhoods vary in size, so ICA turns them into fixed-length feature vectors with aggregation operators such as count or mode. This lets a single local classifier (e.g., a decision tree or an SVM) be reused for every node.
- In the reported setup, GS runs 200 burn-in iterations plus 800 sampling iterations, making it much slower than ICA.
- Related work traces the ideas back to relaxation labeling in computer vision, Besag's iterated conditional modes, and turbo-code decoding (which led to LBP). The authors list heterogeneous networks as an open research direction.
- The text is also available as UMD technical report CS-TR-4905 / UMIACS-TR-2008-04 (University of Maryland and Lawrence Livermore National Laboratory).

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher and Tina Eliassi-Rad. 2008. Collective Classification in Network Data. AI Magazine 29(3).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@article{sen2008collective,
  title = {{Collective Classification in Network Data}},
  author = {Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher and Tina Eliassi-Rad},
  journal = {AI Magazine 29(3)},
  year = {2008},
  doi = {10.1609/aimag.v29i3.2157},
  url = {https://doi.org/10.1609/aimag.v29i3.2157}
}
```

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## References

68 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Aji, S. M., and McEliece, R. J. 2001. The generalized distributive law and free energy minimization. In Proceedings of the 39th Allerton Conference on Communication, Control and Computing. [find](https://scholar.google.com/scholar?q=Aji%2C+S%2E+M%2E%2C+and+McEliece%2C+R%2E+J%2E+2001%2E+The+generalized+distributive+law+and+free+energy+minimization%2E+In+Proceedings+of+the+39th+Allerton+Conference+on+Communication%2C+Control+and+Computing%2E)
2. Anguelov, D.; Taskar, B.; Chatalbashev, V.; Koller, D.; Gupta, D.; Heitz, G.; and Ng, A. 2005. Discriminative learning of markov random fields for segmentation of 3d scan data. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. [find](https://scholar.google.com/scholar?q=Anguelov%2C+D%2E%3B+Taskar%2C+B%2E%3B+Chatalbashev%2C+V%2E%3B+Koller%2C+D%2E%3B+Gupta%2C+D%2E%3B+Heitz%2C+G%2E%3B+and+Ng%2C+A%2E+2005%2E+Discriminative+learning+of+markov+random+fields+for+segmentation+of+3d+scan+data%2E+In+IEEE+Computer+Societ)
3. Barabasi, A.-L., and Albert, R. 2002. Statistical mechanics of complex networks. Reviews of Modern Physics 74:47–97. [find](https://scholar.google.com/scholar?q=Barabasi%2C+A%2E%2DL%2E%2C+and+Albert%2C+R%2E+2002%2E+Statistical+mechanics+of+complex+networks%2E+Reviews+of+Modern+Physics+74%3A47%E2%80%9397%2E)
4. Berrou, C.; Glavieux, A.; and Thitimajshima, P. 1993. Near Shannon limit error-correcting coding and decoding: Turbo codes. In Proceedings of IEEE International Communications Conference. [find](https://scholar.google.com/scholar?q=Berrou%2C+C%2E%3B+Glavieux%2C+A%2E%3B+and+Thitimajshima%2C+P%2E+1993%2E+Near+Shannon+limit+error%2Dcorrecting+coding+and+decoding%3A+Turbo+codes%2E+In+Proceedings+of+IEEE+International+Communications+Conference%2E)
5. Besag, J. 1986. On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. [find](https://scholar.google.com/scholar?q=Besag%2C+J%2E+1986%2E+On+the+statistical+analysis+of+dirty+pictures%2E+Journal+of+the+Royal+Statistical+Society%2E)
6. Boykov, Y.; Veksler, O.; and Zabih, R. 2001. Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence. [find](https://scholar.google.com/scholar?q=Boykov%2C+Y%2E%3B+Veksler%2C+O%2E%3B+and+Zabih%2C+R%2E+2001%2E+Fast+approximate+energy+minimization+via+graph+cuts%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2E)
7. Carvalho, V., and Cohen, W. W. 2005. On the collective classification of email speech acts. In Special Interest Group on Information Retrieval. [find](https://scholar.google.com/scholar?q=Carvalho%2C+V%2E%2C+and+Cohen%2C+W%2E+W%2E+2005%2E+On+the+collective+classification+of+email+speech+acts%2E+In+Special+Interest+Group+on+Information+Retrieval%2E)
8. Chakrabarti, S.; Dom, B.; and Indyk, P. 1998. Enhanced hypertext categorization using hyperlinks. In International Conference on Management of Data. [find](https://scholar.google.com/scholar?q=Chakrabarti%2C+S%2E%3B+Dom%2C+B%2E%3B+and+Indyk%2C+P%2E+1998%2E+Enhanced+hypertext+categorization+using+hyperlinks%2E+In+International+Conference+on+Management+of+Data%2E)
9. Chen, L.; Wainwright, M.; Cetin, M.; and Willsky, A. 2003. Multitarget-multisensor data association using the tree-reweighted max-product algorithm. In SPIE Aerosense conference. [find](https://scholar.google.com/scholar?q=Chen%2C+L%2E%3B+Wainwright%2C+M%2E%3B+Cetin%2C+M%2E%3B+and+Willsky%2C+A%2E+2003%2E+Multitarget%2Dmultisensor+data+association+using+the+tree%2Dreweighted+max%2Dproduct+algorithm%2E+In+SPIE+Aerosense+conference%2E)
10. Cohn, D., and Hofmann, T. 2001. The missing link—a probabilistic model of document content and hypertext connectivity. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Cohn%2C+D%2E%2C+and+Hofmann%2C+T%2E+2001%2E+The+missing+link%E2%80%94a+probabilistic+model+of+document+content+and+hypertext+connectivity%2E+In+Neural+Information+Processing+Systems%2E)
11. Dechter, R. 1996. Bucket elimination: A unifying framework for probabilistic inference. In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Dechter%2C+R%2E+1996%2E+Bucket+elimination%3A+A+unifying+framework+for+probabilistic+inference%2E+In+Proceedings+of+the+Annual+Conference+on+Uncertainty+in+Artificial+Intelligence%2E)
12. Friedman, N.; Getoor, L.; Koller, D.; and Pfeffer, A. 1999. Learning probabilistic relational models. In International Joint Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Friedman%2C+N%2E%3B+Getoor%2C+L%2E%3B+Koller%2C+D%2E%3B+and+Pfeffer%2C+A%2E+1999%2E+Learning+probabilistic+relational+models%2E+In+International+Joint+Conference+on+Artificial+Intelligence%2E)
13. Gallagher, B., and Eliassi-Rad, T. 2007a. An evaluation of experimental methodology for classifiers of relational data. In Workshop on Mining Graphs and Complex Structures, IEEE International Conference on Data Mining (ICDM). [find](https://scholar.google.com/scholar?q=Gallagher%2C+B%2E%2C+and+Eliassi%2DRad%2C+T%2E+2007a%2E+An+evaluation+of+experimental+methodology+for+classifiers+of+relational+data%2E+In+Workshop+on+Mining+Graphs+and+Complex+Structures%2C+IEEE+International+Conferen)
14. Gallagher, B., and Eliassi-Rad, T. 2007b. Leveraging network structure to infer missing values in relational data. Technical Report UCRL-TR-231993, Lawrence Livermore National Laboratory. [find](https://scholar.google.com/scholar?q=Gallagher%2C+B%2E%2C+and+Eliassi%2DRad%2C+T%2E+2007b%2E+Leveraging+network+structure+to+infer+missing+values+in+relational+data%2E+Technical+Report+UCRL%2DTR%2D231993%2C+Lawrence+Livermore+National+Laboratory%2E)
15. Geman, S., and Geman, D. 1984. Stochastic relaxation, gibbs distributions and the bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence. [find](https://scholar.google.com/scholar?q=Geman%2C+S%2E%2C+and+Geman%2C+D%2E+1984%2E+Stochastic+relaxation%2C+gibbs+distributions+and+the+bayesian+restoration+of+images%2E+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2E)
16. Getoor, L.; Friedman, N.; Koller, D.; and Taskar, B. 2001a. Probabilistic models of relational structure. In Proc. ICML01. [find](https://scholar.google.com/scholar?q=Getoor%2C+L%2E%3B+Friedman%2C+N%2E%3B+Koller%2C+D%2E%3B+and+Taskar%2C+B%2E+2001a%2E+Probabilistic+models+of+relational+structure%2E+In+Proc%2E+ICML01%2E)
17. Getoor, L.; Segal, E.; Taskar, B.; and Koller, D. 2001b. Probabilistic models of text and link structure for hypertext classification. In IJCAI Workshop on Text Learning: Beyond Supervision. [find](https://scholar.google.com/scholar?q=Getoor%2C+L%2E%3B+Segal%2C+E%2E%3B+Taskar%2C+B%2E%3B+and+Koller%2C+D%2E+2001b%2E+Probabilistic+models+of+text+and+link+structure+for+hypertext+classification%2E+In+IJCAI+Workshop+on+Text+Learning%3A+Beyond+Supervision%2E)
18. Getoor, L. 2005. Advanced Methods for Knowledge Discovery from Complex Data. Springer. chapter Link-based classification. [find](https://scholar.google.com/scholar?q=Getoor%2C+L%2E+2005%2E+Advanced+Methods+for+Knowledge+Discovery+from+Complex+Data%2E+Springer%2E+chapter+Link%2Dbased+classification%2E)
19. Giles, C. L.; Bollacker, K.; and Lawrence, S. 1998. Citeseer: An automatic citation indexing system. In ACM Digital Libraries. [find](https://scholar.google.com/scholar?q=Giles%2C+C%2E+L%2E%3B+Bollacker%2C+K%2E%3B+and+Lawrence%2C+S%2E+1998%2E+Citeseer%3A+An+automatic+citation+indexing+system%2E+In+ACM+Digital+Libraries%2E)
20. Gilks, W. R.; Richardson, S.; and Spiegelhalter, D. J. 1996. Markov Chain Monte Carlo in Practice. Interdisciplinary Statistics. Chapman & Hall/CRC. [find](https://scholar.google.com/scholar?q=Gilks%2C+W%2E+R%2E%3B+Richardson%2C+S%2E%3B+and+Spiegelhalter%2C+D%2E+J%2E+1996%2E+Markov+Chain+Monte+Carlo+in+Practice%2E+Interdisciplinary+Statistics%2E+Chapman+%26+Hall%2FCRC%2E)
21. Gupta, R.; Diwan, A. A.; and Sarawagi, S. 2007. Efficient inference with cardinality-based clique potentials. In Proceedings of the International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Gupta%2C+R%2E%3B+Diwan%2C+A%2E+A%2E%3B+and+Sarawagi%2C+S%2E+2007%2E+Efficient+inference+with+cardinality%2Dbased+clique+potentials%2E+In+Proceedings+of+the+International+Conference+on+Machine+Learning%2E)
22. Huang, C., and Darwiche, A. 1994. Inference in belief networks: A procedural guide. International Journal of Approximate Reasoning. [find](https://scholar.google.com/scholar?q=Huang%2C+C%2E%2C+and+Darwiche%2C+A%2E+1994%2E+Inference+in+belief+networks%3A+A+procedural+guide%2E+International+Journal+of+Approximate+Reasoning%2E)
23. Hummel, R., and Zucker, S. 1983. On the foundations of relaxation labeling processes. In IEEE Transactions on Pattern Analysis and Machine Intelligence. [find](https://scholar.google.com/scholar?q=Hummel%2C+R%2E%2C+and+Zucker%2C+S%2E+1983%2E+On+the+foundations+of+relaxation+labeling+processes%2E+In+IEEE+Transactions+on+Pattern+Analysis+and+Machine+Intelligence%2E)
24. Jensen, D., and Neville, J. 2002. Linkage and autocorrelation cause feature selection bias in relational learning. In ICML ’02: Proceedings of the Nineteenth International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Jensen%2C+D%2E%2C+and+Neville%2C+J%2E+2002%2E+Linkage+and+autocorrelation+cause+feature+selection+bias+in+relational+learning%2E+In+ICML+%E2%80%9902%3A+Proceedings+of+the+Nineteenth+International+Conference+on+Machine+Learni)
25. Jensen, D.; Neville, J.; and Gallagher, B. 2004. Why collective inference improves relational classification. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [find](https://scholar.google.com/scholar?q=Jensen%2C+D%2E%3B+Neville%2C+J%2E%3B+and+Gallagher%2C+B%2E+2004%2E+Why+collective+inference+improves+relational+classification%2E+In+Proceedings+of+the+10th+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+and+)
26. Jordan, M. I.; Ghahramani, Z.; Jaakkola, T. S.; and Saul, L. K. 1999. An introduction to variational methods for graphical models. Machine Learning. [find](https://scholar.google.com/scholar?q=Jordan%2C+M%2E+I%2E%3B+Ghahramani%2C+Z%2E%3B+Jaakkola%2C+T%2E+S%2E%3B+and+Saul%2C+L%2E+K%2E+1999%2E+An+introduction+to+variational+methods+for+graphical+models%2E+Machine+Learning%2E)
27. Kleinberg, J., and Tardos, E. 1999. Approximation algorithms for classification problems with pairwise relationships: Metric labeling and markov random fields. In IEEE Symposium on Foundations of Computer Science. [find](https://scholar.google.com/scholar?q=Kleinberg%2C+J%2E%2C+and+Tardos%2C+E%2E+1999%2E+Approximation+algorithms+for+classification+problems+with+pairwise+relationships%3A+Metric+labeling+and+markov+random+fields%2E+In+IEEE+Symposium+on+Foundations+of+Comp)
28. Knobbe, A.; deHaas, M.; and Siebes, A. 2001. Propositionalisation and aggregates. In Proceedings of the Fifth European Conference on Principles of Data Mining and Knowledge Discovery. [find](https://scholar.google.com/scholar?q=Knobbe%2C+A%2E%3B+deHaas%2C+M%2E%3B+and+Siebes%2C+A%2E+2001%2E+Propositionalisation+and+aggregates%2E+In+Proceedings+of+the+Fifth+European+Conference+on+Principles+of+Data+Mining+and+Knowledge+Discovery%2E)
29. Kramer, S.; Lavrac, N.; and Flach, P. 2001. Propositionalization approaches to relational data mining. In Dzeroski, S., and Lavrac, N., eds., Relational Data Mining. New York: Springer-Verlag. [find](https://scholar.google.com/scholar?q=Kramer%2C+S%2E%3B+Lavrac%2C+N%2E%3B+and+Flach%2C+P%2E+2001%2E+Propositionalization+approaches+to+relational+data+mining%2E+In+Dzeroski%2C+S%2E%2C+and+Lavrac%2C+N%2E%2C+eds%2E%2C+Relational+Data+Mining%2E+New+York%3A+Springer%2DVerlag%2E)
30. Krogel, M.; Rawles, S.; Zeezny, F.; Flach, P.; Lavrac, N.; and Wrobel, S. 2003. Comparative evaluation of approaches to propositionalization. In International Conference on Inductive Logic Programming. [find](https://scholar.google.com/scholar?q=Krogel%2C+M%2E%3B+Rawles%2C+S%2E%3B+Zeezny%2C+F%2E%3B+Flach%2C+P%2E%3B+Lavrac%2C+N%2E%3B+and+Wrobel%2C+S%2E+2003%2E+Comparative+evaluation+of+approaches+to+propositionalization%2E+In+International+Conference+on+Inductive+Logic+Programming)
31. Kschischang, F. R., and Frey, B. J. 1998. Iterative decoding of compound codes by probability progation in graphical models. IEEE Journal on Selected Areas in Communication. [find](https://scholar.google.com/scholar?q=Kschischang%2C+F%2E+R%2E%2C+and+Frey%2C+B%2E+J%2E+1998%2E+Iterative+decoding+of+compound+codes+by+probability+progation+in+graphical+models%2E+IEEE+Journal+on+Selected+Areas+in+Communication%2E)
32. Kschischang, F. R.; Frey, B. J.; and Loeliger, H. A. 2001. Factor graphs and the sum-product algorithm. In IEEE Transactions on Information Theory. [find](https://scholar.google.com/scholar?q=Kschischang%2C+F%2E+R%2E%3B+Frey%2C+B%2E+J%2E%3B+and+Loeliger%2C+H%2E+A%2E+2001%2E+Factor+graphs+and+the+sum%2Dproduct+algorithm%2E+In+IEEE+Transactions+on+Information+Theory%2E)
33. Lafferty, J. D.; McCallum, A.; and Pereira, F. C. N. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Lafferty%2C+J%2E+D%2E%3B+McCallum%2C+A%2E%3B+and+Pereira%2C+F%2E+C%2E+N%2E+2001%2E+Conditional+random+fields%3A+Probabilistic+models+for+segmenting+and+labeling+sequence+data%2E+In+Proceedings+of+the+International+Conference+on+)
34. Li, S.; Wang, H.; and Petrou, M. 1994. Relaxation labeling of markov random fields. In In Proceedings of International Conference Pattern Recognition, volume 94. [find](https://scholar.google.com/scholar?q=Li%2C+S%2E%3B+Wang%2C+H%2E%3B+and+Petrou%2C+M%2E+1994%2E+Relaxation+labeling+of+markov+random+fields%2E+In+In+Proceedings+of+International+Conference+Pattern+Recognition%2C+volume+94%2E)
35. Lu, Q., and Getoor, L. 2003. Link based classification. In Proceedings of the International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Lu%2C+Q%2E%2C+and+Getoor%2C+L%2E+2003%2E+Link+based+classification%2E+In+Proceedings+of+the+International+Conference+on+Machine+Learning%2E)
36. Macskassy, S., and Provost, F. 2007. Classification in networked data: A toolkit and a univariate case study. Journal of Machine Learning Research. [find](https://scholar.google.com/scholar?q=Macskassy%2C+S%2E%2C+and+Provost%2C+F%2E+2007%2E+Classification+in+networked+data%3A+A+toolkit+and+a+univariate+case+study%2E+Journal+of+Machine+Learning+Research%2E)
37. Macskassy, S. A. 2007. Improving learning in networked data by combining explicit and mined links. In Proceedings of the Twenty-Second Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Macskassy%2C+S%2E+A%2E+2007%2E+Improving+learning+in+networked+data+by+combining+explicit+and+mined+links%2E+In+Proceedings+of+the+Twenty%2DSecond+Conference+on+Artificial+Intelligence%2E)
38. McCallum, A.; Nigam, K.; Rennie, J.; and Seymore, K. 2000. Automating the construction of internet portals with machine learning. Information Retrieval Journal. [find](https://scholar.google.com/scholar?q=McCallum%2C+A%2E%3B+Nigam%2C+K%2E%3B+Rennie%2C+J%2E%3B+and+Seymore%2C+K%2E+2000%2E+Automating+the+construction+of+internet+portals+with+machine+learning%2E+Information+Retrieval+Journal%2E)
39. McDowell, L. K.; Gupta, K. M.; and Aha, D. W. 2007. Cautious inference in collective classification. In Proceedings of AAAI. [find](https://scholar.google.com/scholar?q=McDowell%2C+L%2E+K%2E%3B+Gupta%2C+K%2E+M%2E%3B+and+Aha%2C+D%2E+W%2E+2007%2E+Cautious+inference+in+collective+classification%2E+In+Proceedings+of+AAAI%2E)
40. McEliece, R. J.; MacKay, D. J. C.; and Cheng, J. F. 1998. Turbo decoding as an instance of Pearl’s belief propagation algorithm. IEEE Journal on Selected Areas in Communication. [find](https://scholar.google.com/scholar?q=McEliece%2C+R%2E+J%2E%3B+MacKay%2C+D%2E+J%2E+C%2E%3B+and+Cheng%2C+J%2E+F%2E+1998%2E+Turbo+decoding+as+an+instance+of+Pearl%E2%80%99s+belief+propagation+algorithm%2E+IEEE+Journal+on+Selected+Areas+in+Communication%2E)
41. McPherson, M.; Smith-Lovin, L.; and Cook, J. M. 2001. Birds of a feather: Homophily in social networks. Annual Review of Sociology 27. This article consists of 30 page(s). [find](https://scholar.google.com/scholar?q=McPherson%2C+M%2E%3B+Smith%2DLovin%2C+L%2E%3B+and+Cook%2C+J%2E+M%2E+2001%2E+Birds+of+a+feather%3A+Homophily+in+social+networks%2E+Annual+Review+of+Sociology+27%2E+This+article+consists+of+30+page%28s%29%2E)
42. Minka, T. 2001. Expectation propagation for approximate bayesian inference. In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Minka%2C+T%2E+2001%2E+Expectation+propagation+for+approximate+bayesian+inference%2E+In+Proceedings+of+the+Annual+Conference+on+Uncertainty+in+Artificial+Intelligence%2E)
43. Mooij, J. M., and Kappen, H. J. 2004. Validity estimates for loopy belief propagation on binary real-world networks. In NIPS. [find](https://scholar.google.com/scholar?q=Mooij%2C+J%2E+M%2E%2C+and+Kappen%2C+H%2E+J%2E+2004%2E+Validity+estimates+for+loopy+belief+propagation+on+binary+real%2Dworld+networks%2E+In+NIPS%2E)
44. Neville, J., and Jensen, D. 2000. Iterative classification in relational data. In Workshop on Statistical Relational Learning, AAAI. [find](https://scholar.google.com/scholar?q=Neville%2C+J%2E%2C+and+Jensen%2C+D%2E+2000%2E+Iterative+classification+in+relational+data%2E+In+Workshop+on+Statistical+Relational+Learning%2C+AAAI%2E)
45. Neville, J., and Jensen, D. 2007a. Bias/variance analysis for relational domains. In International Conference on Inductive Logic Programming. [find](https://scholar.google.com/scholar?q=Neville%2C+J%2E%2C+and+Jensen%2C+D%2E+2007a%2E+Bias%2Fvariance+analysis+for+relational+domains%2E+In+International+Conference+on+Inductive+Logic+Programming%2E)
46. Neville, J., and Jensen, D. 2007b. Relational dependency networks. Journal of Machine Learning Research. [find](https://scholar.google.com/scholar?q=Neville%2C+J%2E%2C+and+Jensen%2C+D%2E+2007b%2E+Relational+dependency+networks%2E+Journal+of+Machine+Learning+Research%2E)
47. Pearl, J. 1988. Probabilistic reasoning in intelligent systems. In Morgan Kaufmann, San Fansisco. [find](https://scholar.google.com/scholar?q=Pearl%2C+J%2E+1988%2E+Probabilistic+reasoning+in+intelligent+systems%2E+In+Morgan+Kaufmann%2C+San+Fansisco%2E)
48. Perlich, C., and Provost, F. 2003. Aggregation-based feature invention and relational concept classes. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. [find](https://scholar.google.com/scholar?q=Perlich%2C+C%2E%2C+and+Provost%2C+F%2E+2003%2E+Aggregation%2Dbased+feature+invention+and+relational+concept+classes%2E+In+ACM+SIGKDD+International+Conference+on+Knowledge+Discovery+and+Data+Mining%2E)
49. Perlich, C., and Provost, F. 2006. Distribution-based aggregation for relational learning with identifier attributes. Machine Learning Journal. [find](https://scholar.google.com/scholar?q=Perlich%2C+C%2E%2C+and+Provost%2C+F%2E+2006%2E+Distribution%2Dbased+aggregation+for+relational+learning+with+identifier+attributes%2E+Machine+Learning+Journal%2E)
50. Popescul, A., and Ungar, L. 2003. Structural logistic regression for link analysis. In KDD Workshop on Multi-Relational Data Mining. [find](https://scholar.google.com/scholar?q=Popescul%2C+A%2E%2C+and+Ungar%2C+L%2E+2003%2E+Structural+logistic+regression+for+link+analysis%2E+In+KDD+Workshop+on+Multi%2DRelational+Data+Mining%2E)
51. Richardson, M., and Domingos, P. 2006. Markov logic networks. Machine Learning. [find](https://scholar.google.com/scholar?q=Richardson%2C+M%2E%2C+and+Domingos%2C+P%2E+2006%2E+Markov+logic+networks%2E+Machine+Learning%2E)
52. Sen, P., and Getoor, L. 2006. Empirical comparison of approximate inference algorithms for networked data. In ICML workshop on Open Problems in Statistical Relational Learning (SRL2006). [find](https://scholar.google.com/scholar?q=Sen%2C+P%2E%2C+and+Getoor%2C+L%2E+2006%2E+Empirical+comparison+of+approximate+inference+algorithms+for+networked+data%2E+In+ICML+workshop+on+Open+Problems+in+Statistical+Relational+Learning+%28SRL2006%29%2E)
53. Sen, P., and Getoor, L. 2007. Link-based classification. Technical Report CS-TR-4858, University of Maryland. [find](https://scholar.google.com/scholar?q=Sen%2C+P%2E%2C+and+Getoor%2C+L%2E+2007%2E+Link%2Dbased+classification%2E+Technical+Report+CS%2DTR%2D4858%2C+University+of+Maryland%2E)
54. Slattery, S., and Craven, M. 1998. Combining statistical and relational methods for learning in hypertext domains. In International Conference on Inductive Logic Programming. [find](https://scholar.google.com/scholar?q=Slattery%2C+S%2E%2C+and+Craven%2C+M%2E+1998%2E+Combining+statistical+and+relational+methods+for+learning+in+hypertext+domains%2E+In+International+Conference+on+Inductive+Logic+Programming%2E)
55. Taskar, B.; Abbeel, P.; and Koller, D. 2002. Discriminative probabilistic models for relational data. In Proceedings of the Annual Conference on Uncertainty in Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Taskar%2C+B%2E%3B+Abbeel%2C+P%2E%3B+and+Koller%2C+D%2E+2002%2E+Discriminative+probabilistic+models+for+relational+data%2E+In+Proceedings+of+the+Annual+Conference+on+Uncertainty+in+Artificial+Intelligence%2E)
56. Taskar, B.; Wong, M. F.; Abbeel, P.; and Koller, D. 2003. Link prediction in relational data. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Taskar%2C+B%2E%3B+Wong%2C+M%2E+F%2E%3B+Abbeel%2C+P%2E%3B+and+Koller%2C+D%2E+2003%2E+Link+prediction+in+relational+data%2E+In+Neural+Information+Processing+Systems%2E)
57. Taskar, B.; Chatalbashev, V.; Koller, D.; and Guestrin, C. 2005. Learning structured prediction models: A large margin approach. In Proceedings of the International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Taskar%2C+B%2E%3B+Chatalbashev%2C+V%2E%3B+Koller%2C+D%2E%3B+and+Guestrin%2C+C%2E+2005%2E+Learning+structured+prediction+models%3A+A+large+margin+approach%2E+In+Proceedings+of+the+International+Conference+on+Machine+Learning%2E)
58. Taskar, B.; Guestrin, C.; and Koller, D. 2003. Max-margin markov networks. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Taskar%2C+B%2E%3B+Guestrin%2C+C%2E%3B+and+Koller%2C+D%2E+2003%2E+Max%2Dmargin+markov+networks%2E+In+Neural+Information+Processing+Systems%2E)
59. Taskar, B.; Segal, E.; and Koller, D. 2001. Probabilistic classification and clustering in relational data. In Proceedings of the International Joint Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Taskar%2C+B%2E%3B+Segal%2C+E%2E%3B+and+Koller%2C+D%2E+2001%2E+Probabilistic+classification+and+clustering+in+relational+data%2E+In+Proceedings+of+the+International+Joint+Conference+on+Artificial+Intelligence%2E)
60. Wainwright, M. J.; Jaakkola, T. S.; and Willsky, A. S. 2005. Map estimation via agreement on (hyper)trees: Message-passing and linear-programming approaches. In IEEE Transactions on Information Theory. [find](https://scholar.google.com/scholar?q=Wainwright%2C+M%2E+J%2E%3B+Jaakkola%2C+T%2E+S%2E%3B+and+Willsky%2C+A%2E+S%2E+2005%2E+Map+estimation+via+agreement+on+%28hyper%29trees%3A+Message%2Dpassing+and+linear%2Dprogramming+approaches%2E+In+IEEE+Transactions+on+Information+Theory)
61. Weiss, Y. 2001. Advanced Mean Field Methods, Manfred Opper and David Saad (eds). MIT Press. chapter Comparing the mean field method and belief propagation for approximate inference in MRFs. [find](https://scholar.google.com/scholar?q=Weiss%2C+Y%2E+2001%2E+Advanced+Mean+Field+Methods%2C+Manfred+Opper+and+David+Saad+%28eds%29%2E+MIT+Press%2E+chapter+Comparing+the+mean+field+method+and+belief+propagation+for+approximate+inference+in+MRFs%2E)
62. Xu, L.; Wilkinson, D.; Southey, F.; and Schuurmans, D. 2006. Discriminative unsupervised learning of structured predictors. In Proceedings of the International Conference on Machine Learning. [find](https://scholar.google.com/scholar?q=Xu%2C+L%2E%3B+Wilkinson%2C+D%2E%3B+Southey%2C+F%2E%3B+and+Schuurmans%2C+D%2E+2006%2E+Discriminative+unsupervised+learning+of+structured+predictors%2E+In+Proceedings+of+the+International+Conference+on+Machine+Learning%2E)
63. Yang, Y.; Slattery, S.; and Ghani, R. 2002. A study of approaches to hypertext categorization. Journal of Intelligent Information Systems. [find](https://scholar.google.com/scholar?q=Yang%2C+Y%2E%3B+Slattery%2C+S%2E%3B+and+Ghani%2C+R%2E+2002%2E+A+study+of+approaches+to+hypertext+categorization%2E+Journal+of+Intelligent+Information+Systems%2E)
64. Yanover, C., and Weiss, Y. 2002. Approximate inference and protein-folding. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Yanover%2C+C%2E%2C+and+Weiss%2C+Y%2E+2002%2E+Approximate+inference+and+protein%2Dfolding%2E+In+Neural+Information+Processing+Systems%2E)
65. Yedidia, J.; Freeman, W.; and Weiss, Y. 2005. Constructing free-energy approximations and generalized belief propagation algorithms. In IEEE Transactions on Information Theory. [find](https://scholar.google.com/scholar?q=Yedidia%2C+J%2E%3B+Freeman%2C+W%2E%3B+and+Weiss%2C+Y%2E+2005%2E+Constructing+free%2Denergy+approximations+and+generalized+belief+propagation+algorithms%2E+In+IEEE+Transactions+on+Information+Theory%2E)
66. Yedidia, J.; W.T.Freeman; and Weiss, Y. 2000. Generalized belief propagation. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Yedidia%2C+J%2E%3B+W%2ET%2EFreeman%3B+and+Weiss%2C+Y%2E+2000%2E+Generalized+belief+propagation%2E+In+Neural+Information+Processing+Systems%2E)
67. Yuille, A. L. 2002. CCCP algorithms to minimize the bethe and kikuchi free energies: Convergent alternatives to belief propagation. In Neural Information Processing Systems. [find](https://scholar.google.com/scholar?q=Yuille%2C+A%2E+L%2E+2002%2E+CCCP+algorithms+to+minimize+the+bethe+and+kikuchi+free+energies%3A+Convergent+alternatives+to+belief+propagation%2E+In+Neural+Information+Processing+Systems%2E)
68. Zhang, N. L., and Poole, D. 1994. A simple approach to bayesian network computations. In Canadian Conference on Artificial Intelligence. [find](https://scholar.google.com/scholar?q=Zhang%2C+N%2E+L%2E%2C+and+Poole%2C+D%2E+1994%2E+A+simple+approach+to+bayesian+network+computations%2E+In+Canadian+Conference+on+Artificial+Intelligence%2E)

*Updated 2 November 2026*