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Structure and inference in annotated networks

Author

Listed:
  • M. E. J. Newman

    (University of Michigan
    Center for the Study of Complex Systems, University of Michigan
    Santa Fe Institute)

  • Aaron Clauset

    (Santa Fe Institute
    University of Colorado
    BioFrontiers Institute, University of Colorado)

Abstract

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.

Suggested Citation

  • M. E. J. Newman & Aaron Clauset, 2016. "Structure and inference in annotated networks," Nature Communications, Nature, vol. 7(1), pages 1-11, September.
    • Handle: RePEc:nat:natcom:v:7:y:2016:i:1:d:10.1038_ncomms11863
    DOI: 10.1038/ncomms11863
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    Cited by:

    1. Yunpeng Zhao & Qing Pan & Chengan Du, 2019. "Logistic regression augmented community detection for network data with application in identifying autism‐related gene pathways," Biometrics, The International Biometric Society, vol. 75(1), pages 222-234, March.
    2. Ma, Xiaoke & Li, Dongyuan & Tan, Shiyin & Huang, Zhihao, 2019. "Detecting evolving communities in dynamic networks using graph regularized evolutionary nonnegative matrix factorization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 530(C), pages 1-1.
    3. Xu, Xiao-Ting & Wang, Nianxin & Bian, Jun & Zhou, Bin, 2019. "Understanding the diversity on power-law-like degree distribution in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 576-581.
    4. Alireza Ermagun & Nazanin Tajik, 2021. "Recovery patterns and physics of the network," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-20, January.
    5. Boris Mirkin & Soroosh Shalileh, 2022. "Community Detection in Feature-Rich Networks Using Data Recovery Approach," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 432-462, November.
    6. Hric, Darko & Kaski, Kimmo & Kivelä, Mikko, 2018. "Stochastic block model reveals maps of citation patterns and their evolution in time," Journal of Informetrics, Elsevier, vol. 12(3), pages 757-783.
    7. Chang, Zhenhai & Yin, Xianjun & Jia, Caiyan & Wang, Xiaoyang, 2018. "Mixture models with entropy regularization for community detection in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 496(C), pages 339-350.
    8. Fengqin Tang & Chunning Wang & Jinxia Su & Yuanyuan Wang, 2020. "Spectral clustering-based community detection using graph distance and node attributes," Computational Statistics, Springer, vol. 35(1), pages 69-94, March.
    9. Jun Liu & Jiangzhou Wang & Binghui Liu, 2020. "Community Detection of Multi-Layer Attributed Networks via Penalized Alternating Factorization," Mathematics, MDPI, vol. 8(2), pages 1-20, February.
    10. Mirko Signorelli & Luisa Cutillo, 2022. "On community structure validation in real networks," Computational Statistics, Springer, vol. 37(3), pages 1165-1183, July.
    11. D’Ambra, Pasqua & Vassilevski, Panayot S. & Cutillo, Luisa, 2023. "Extending bootstrap AMG for clustering of attributed graphs," Applied Mathematics and Computation, Elsevier, vol. 447(C).
    12. Junhui Cai & Dan Yang & Wu Zhu & Haipeng Shen & Linda Zhao, 2021. "Network regression and supervised centrality estimation," Papers 2111.12921, arXiv.org.
    13. Sirio Legramanti & Tommaso Rigon & Daniele Durante, 2022. "Bayesian Testing for Exogenous Partition Structures in Stochastic Block Models," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 84(1), pages 108-126, June.
    14. Zhou, Bin & Yan, Xiao-Yong & Xu, Xiao-Ke & Xu, Xiao-Ting & Wang, Nianxin, 2018. "Evolutionary of online social networks driven by pareto wealth distribution and bidirectional preferential attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 427-434.
    15. Lucy L. Gao & Daniela Witten & Jacob Bien, 2022. "Testing for association in multiview network data," Biometrics, The International Biometric Society, vol. 78(3), pages 1018-1030, September.
    16. Joseph Crawford & Tijana Milenković, 2018. "ClueNet: Clustering a temporal network based on topological similarity rather than denseness," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-25, May.
    17. Anna Badalyan & Nicolò Ruggeri & Caterina De Bacco, 2024. "Structure and inference in hypergraphs with node attributes," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    18. Tian, Yahui & Gel, Yulia R., 2019. "Fusing data depth with complex networks: Community detection with prior information," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 99-116.
    19. Martina Contisciani & Federico Battiston & Caterina De Bacco, 2022. "Inference of hyperedges and overlapping communities in hypergraphs," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    20. Thorben Funke & Till Becker, 2019. "Stochastic block models: A comparison of variants and inference methods," PLOS ONE, Public Library of Science, vol. 14(4), pages 1-40, April.
    21. Zhou, Bin & Xu, Xiao-Ting & Liu, Jian-Guo & Xu, Xiao-Ke & Wang, Nianxin, 2019. "Information interaction model for the mobile communication networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 1170-1176.

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