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Structure and inference in annotated networks
Open accessAbstract
For many networks of scientific interest we know both the connections of the network and information about the network nodes, such as the age or gender of individuals in a social network. Here we demonstrate how this ‘metadata’ can be used to improve our understanding of network structure. We focus in particular on the problem of community detection in networks and develop a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone. Crucially, the method does not assume that the metadata are correlated with the communities we are trying to find. Instead, the method learns whether a correlation exists and correctly uses or ignores the metadata depending on whether they contain useful information. We demonstrate our method on synthetic networks with known structure and on real-world networks, large and small, drawn from social, biological and technological domains.
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Citations
Cites
Elements of Information Theory
Cover, T. M., & Thomas, J. A. (2005) · doi:10.1002/047174882x · Cited by 13,791
Fortunato, S. (2010) · Physics Reports · doi:10.1016/j.physrep.2009.11.002 · Cited by 11,293
Network motifs: simple building blocks of complex networks.
Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., & Alon, U. (2002) · Science · doi:10.1126/science.298.5594.824 · Cited by 7,368
Functional cartography of complex metabolic networks
Guimerà, R., & Nunes Amaral, L. A. (2005) · Nature · doi:10.1038/nature03288 · Cited by 3,983 · cited 5×
“(As we have said, the right-hand side of (6) becomes equal to the left, and hence equal to the true loglikelihood, when q(s) is given the value in equation (7).”
BackgroundMethodInfluentialComparing community structure identification
Danon, L., Díaz-Guilera, A., Duch, J., & Arenas, A. (2005) · Journal of Statistical Mechanics: Theory and Experiment · doi:10.1088/1742-5468/2005/09/p09008 · Cited by 2,872
Stochastic blockmodels: First steps
Holland, P. W., Laskey, K. B., & Leinhardt, S. (1983) · Social Networks · doi:10.1016/0378-8733(83)90021-7 · Cited by 2,755
(2011) · Choice Reviews Online · doi:10.5860/choice.48-3328 · Cited by 2,736
The political blogosphere and the 2004 U.S. election: divided they blog
Adamic, L. A., & Glance, N. (2005) · Proceedings of the 3rd international workshop on Link discovery · doi:10.1145/1134271.1134277 · Cited by 2,619
Barthélemy, M. (2011) · Physics Reports · doi:10.1016/j.physrep.2010.11.002 · Cited by 2,469
Models of core/periphery structures
Borgatti, S. P., & Everett, M. G. (2000) · Social Networks · doi:10.1016/s0378-8733(99)00019-2 · Cited by 2,219
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
Self-citationStochastic blockmodels and community structure in networks
Karrer, B., & Newman, M. E. J. (2011) · Physical Review E · doi:10.1103/physreve.83.016107 · Cited by 2,025
Self-citationLatent Space Approaches to Social Network Analysis
Hoff, P. D., Raftery, A. E., & Handcock, M. S. (2002) · Journal of the American Statistical Association · doi:10.1198/016214502388618906 · Cited by 1,917
Race, School Integration, and Friendship Segregation in America
Moody, J. (2001) · American Journal of Sociology · doi:10.1086/338954 · Cited by 1,342
Understanding belief propagation and its generalizations
Yedidia, J. S., Freeman, W. T., & Weiss, Y. (2003) · Cited by 1,288
Body size in ecological networks
WOODWARD, G., EBENMAN, B., EMMERSON, M., MONTOYA, J., OLESEN, J., VALIDO, A., & WARREN, P. (2005) · Trends in Ecology & Evolution · doi:10.1016/j.tree.2005.04.005 · Cited by 1,251
Performance of modularity maximization in practical contexts
Good, B. H., de Montjoye, Y.-A., & Clauset, A. (2010) · Physical Review E · doi:10.1103/physreve.81.046106 · Cited by 937
Self-citation4 Community Detection in Networks with Node Attributes
Yang, J., McAuley, J., & Leskovec, J. (2013) · Cited by 803
Defining and evaluating network communities based on ground-truth
Yang, J., & Leskovec, J. (2012) · Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics · doi:10.1145/2350190.2350193 · Cited by 762
Social structure of Facebook networks
Traud, A. L., Mucha, P. J., & Porter, M. A. (2012) · Physica A: Statistical Mechanics and its Applications · doi:10.1016/j.physa.2011.12.021 · Cited by 707
Newman, M. E. J. (2010) · Networks · doi:10.1093/acprof:oso/9780199206650.003.0001 · Cited by 691
Self-citationCommunity Detection in Networks with Node Attributes
Yang, J., McAuley, J., & Leskovec, J. (2013) · 2013 IEEE 13th International Conference on Data Mining · doi:10.1109/icdm.2013.167 · Cited by 575
Inference and Phase Transitions in the Detection of Modules in Sparse Networks
Decelle, A., Krzakala, F., Moore, C., & Zdeborová, L. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.065701 · Cited by 392
Subnetwork hierarchies of biochemical pathways
Holme, P., Huss, M., & Jeong, H. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg033 · Cited by 367
Uncovering space-independent communities in spatial networks
Expert, P., Evans, T. S., Blondel, V. D., & Lambiotte, R. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1018962108 · Cited by 350
Reconstruction and estimation in the planted partition model
Mossel, E., Neeman, J., & Sly, A. (2015) · Probability Theory and Related Fields · doi:10.1007/s00440-014-0576-6 · Cited by 301
Community-Affiliation Graph Model for Overlapping Network Community Detection
Yang, J., & Leskovec, J. (2012) · 2012 IEEE 12th International Conference on Data Mining · doi:10.1109/icdm.2012.139 · Cited by 293
Learning Latent Block Structure in Weighted NetworksPreprint
Aicher, C., Jacobs, A. Z., & Clauset, A. J. (2016) · Cited by 271
Self-citationClustering attributed graphs: Models, measures and methods
BOTHOREL, C., CRUZ, J. D., MAGNANI, M., & MICENKOVÁ, B. (2015) · Network Science · doi:10.1017/nws.2015.9 · Cited by 249
Learning latent block structure in weighted networks
Aicher, C., Jacobs, A. Z., & Clauset, A. (2015) · Journal of Complex Networks · doi:10.1093/comnet/cnu026 · Cited by 238
Self-citationCommunity detection in networks: Structural communities versus ground truth
Hric, D., Darst, R. K., & Fortunato, S. (2014) · Physical Review E · doi:10.1103/physreve.90.062805 · Cited by 238
Normalized Mutual Information to evaluate overlapping community finding algorithms
McDaid, A. F., Greene, D., & Hurley, N. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1110.2515 · Cited by 225
Plasmodium falciparum Variant Surface Antigen Expression Patterns during Malaria
Bull, P. C., Berriman, M., Kyes, S., Quail, M. A., Hall, N., Kortok, M. M., Marsh, K., & Newbold, C. I. (2005) · PLoS Pathogens · doi:10.1371/journal.ppat.0010026 · Cited by 189
Efficiently inferring community structure in bipartite networks
Larremore, D. B., Clauset, A., & Jacobs, A. Z. (2014) · Physical Review E · doi:10.1103/physreve.90.012805 · Cited by 182
Self-citationPlasmodium falciparum var gene expression is modified by host immunity
Warimwe, G. M., Keane, T., Fegan, G., Musyoki, J., Newton, C. R. J. C., Pain, A., Berriman, M., Marsh, K., & Bull, P. C. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0907590106 · Cited by 154
Semi-supervised clustering algorithm for community structure detection in complex networks
Ma, X., Gao, L., Yong, X., & Fu, L. (2009) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2009.09.018 · Cited by 145
Covariate-assisted spectral clustering
Binkiewicz, N., Vogelstein, J. T., & Rohe, K. (2017) · Biometrika · doi:10.1093/biomet/asx008 · Cited by 142
BODY SIZES OF CONSUMERS AND THEIR RESOURCES
Brose, U., Cushing, L. J., Berlow, E. L., Jönsson, T., Banašek‐Richter, C., Bersier, L., Blanchard, J. L., Brey, T., Carpenter, S. R., Cattin, M.-F., et al. (2005) · Ecology · doi:10.1890/05-0379 · Cited by 128
Emergence of Soft Communities from Geometric Preferential Attachment
Zuev, K. M., Boguñá, M., Bianconi, G., & Krioukov, D. (2015) · Scientific Reports · doi:10.1038/srep09421 · Cited by 115
A Network Approach to Analyzing Highly Recombinant Malaria Parasite Genes
Larremore, D. B., Clauset, A. J., & Buckee, C. O. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003268 · Cited by 105
Self-citationEmergence of soft communities from geometric preferential attachment
Zuev, K. M., Boguñá, M., Ginestra Bianconi, A., & Krioukov, D. (2015) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · Cited by 93
Social Structure of Facebook NetworksPreprint
Traud, A. L., Mucha, P. J., & Porter, M. A. (2011) · SSRN Electronic Journal · doi:10.2139/ssrn.1470768 · Cited by 71
A Spin-Glass Model for Semi-Supervised Community Detection
Eaton, E., & Mansbach, R. A. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v26i1.8320 · Cited by 65
An approach to classifying sequence tags sampled from Plasmodium falciparum var genes
Bull, P. C., Kyes, S., Buckee, C. O., Montgomery, J. L., Kortok, M., Newbold, C., & Marsh, K. (2007) · Molecular and Biochemical Parasitology · doi:10.1016/j.molbiopara.2007.03.011 · Cited by 64
Overlapping community detection in labeled graphs
Galbrun, E., Gionis, A., & Tatti, N. (2014) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0373-y · Cited by 62
Phase transitions in semisupervised clustering of sparse networks
Zhang, P., Moore, C., & Zdeborová, L. (2014) · Physical Review E · doi:10.1103/physreve.90.052802 · Cited by 55
Active learning for node classification in assortative and disassortative networks
Moore, C., Yan, X., Zhu, Y., Rouquier, J., & Lane, T. D. R. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020552 · Cited by 50
Trophic Dynamics of Antarctic Shelf Ecosystems - Food Webs and Energy Flow Budgets
Jacob, U. (2005) · Cited by 32
Active Semi-supervised Community Detection Algorithm with Label Propagation
Leng, M., Yao, Y., Cheng, J., Lv, W., & Chen, X. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37450-0_25 · Cited by 25
Semi-supervised Eigenvectors for Large-scale Locally-biased LearningPreprint
Hansen, T. J., & Mahoney, M. W. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1304.7528 · Cited by 13
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Community detection in networks: a user guide
Fortunato, S., & Hric, D. (2016) · Physics Reports · doi:10.1016/j.physrep.2016.09.002 · Cited by 2,086
Network analysis of multivariate data in psychological science
Borsboom, D., Deserno, M. K., Rhemtulla, M., Epskamp, S., Fried, E. I., McNally, R. J., Robinaugh, D. J., Perugini, M., Dalege, J., Costantini, G., et al. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00055-w · Cited by 1,371
A Comparative Analysis of Community Detection Algorithms on Artificial Networks
Yang, Z., Algesheimer, R., & Tessone, C. J. (2016) · Scientific Reports · doi:10.1038/srep30750 · Cited by 578
From maps to multi-dimensional network mechanisms of mental disorders
Braun, U., Schaefer, A., Betzel, R. F., Tost, H., Meyer-Lindenberg, A., & Bassett, D. S. (2018) · Neuron · doi:10.1016/j.neuron.2017.11.007 · Cited by 177
Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison
De Domenico, M., & Biamonte, J. (2016) · Physical Review X · doi:10.1103/physrevx.6.041062 · Cited by 160
Metadata
- Authors
- Newman, M. E. J., & Clauset, A.
- Venue
- Nature Communications
- Published
- 2016-06-16
- Volume
- 7
- Issue
- 1
- Pages
- 11863-11863
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W1930572491
- Cited by
- 379
- References
- 50
- FWCI
- 29.97
- Type
- journal article
- Language
- en
- Open access
- gold
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