skip to main content
10.1145/3097983.3098116acmconferencesArticle/Chapter ViewAbstractPublication PageskddConference Proceedingsconference-collections
research-article
Public Access

Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

Authors Info & Claims
Published:04 August 2017Publication History

ABSTRACT

A widely recognized organizing principle of networks is structural homophily, which suggests that people with more common neighbors are more likely to connect with each other. However, what influence the diverse structures embedded in common neighbors have on link formation is much less well-understood. To explore this problem, we begin by characterizing the structural diversity of common neighborhoods. Using a collection of 120 large-scale networks, we demonstrate that the impact of the common neighborhood diversity on link existence can vary substantially across networks. We find that its positive effect on Facebook and negative effect on LinkedIn suggest different underlying networking needs in these networks. We also discover striking cases where diversity violates the principle of homophily---that is, where fewer mutual connections may lead to a higher tendency to link with each other. We then leverage structural diversity to develop a common neighborhood signature (CNS), which we apply to a large set of networks to uncover unique network superfamilies not discoverable by conventional methods. Our findings shed light on the pursuit to understand the ways in which network structures are organized and formed, pointing to potential advancement in designing graph generation models and recommender systems.

References

  1. Lada A. Adamic and Eytan Adar 2001. Friends and Neighbors on the Web. SOCIAL NETWORKS Vol. 25 (2001), 211--230. Google ScholarGoogle ScholarCross RefCross Ref
  2. Nicomachean Ethics1162a Aristotle and VIII Book 1934. Aristotle in 23 Volumes, Vol. 19, translated by H. Rackham. (1934).Google ScholarGoogle Scholar
  3. Lars Backstrom and Jon M. Kleinberg 2014. Romantic Partnerships and the Dispersion of social ties: a netwrok analysis of relationship status on Facebook. In CSCW '14. 831--841. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Albert-Laszlo Barabasi and Reka Albert 1999. Emergence of Scaling in Random Networks. Science, Vol. 286, 5439 (1999), 509--512. Google ScholarGoogle ScholarCross RefCross Ref
  5. Carlo Vittorio Cannistraci, Gregorio Alanis-Lobato, and Timothy Ravasi 2013. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks. Scientific reports Vol. 3 (2013). Google ScholarGoogle ScholarCross RefCross Ref
  6. Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, and Nitesh V. Chawla 2014. Inferring User Demographics and Social Strategies in Mobile Social Networks KDD'14. ACM, 15--24.Google ScholarGoogle Scholar
  7. Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, and Bai Wang 2015. CoupledLP: Link Prediction in Coupled Networks. In KDD '15. ACM, 199--208. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. David Easley and Jon Kleinberg 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge University Press. Google ScholarGoogle ScholarCross RefCross Ref
  9. P ErdHos and A Rényi 1959. On random graphs I. Publ. Math. Debrecen Vol. 6 (1959), 290--297.Google ScholarGoogle Scholar
  10. Péter L. Erdös, István Miklós, and Zoltán Toroczkai 2016. New classes of degree sequences with fast mixing swap Markov chain sampling. CoRR Vol. abs/1601.08224 (2016). showURL%http://arxiv.org/abs/1601.08224Google ScholarGoogle Scholar
  11. Michalis Faloutsos, Petros Faloutsos, and Christos Faloutsos. 1999. On power-law relationships of the Internet topology SIGCOMM'99. 251--262.Google ScholarGoogle Scholar
  12. Zhanpeng Fang, Xinyu Zhou, Jie Tang, Wei Shao, A.C.M. Fong, Longjun Sun, Ying Ding, Ling Zhou, and Jarder Luo 2014. Modeling Paying Behavior in Game Social Networks. CIKM '14. 411--420.Google ScholarGoogle Scholar
  13. Mark Granovetter. 1985. Economic Action and Social Structure: The Problem of Embeddedness. The American Journal of Sociology (1985).Google ScholarGoogle Scholar
  14. Mark Granovetter. 1992. Problems of explanation in economic sociology. Networks and organizations: Structure, form, and action Vol. 25 (1992), 56.Google ScholarGoogle Scholar
  15. Emily M. Jin, Michelle Girvan, and M. E. J. Newman. 2001. Structure of growing social networks. Phys. Rev. E Vol. 64 (2001), 046132. Issue 4.Google ScholarGoogle ScholarCross RefCross Ref
  16. Xiangnan Kong and Philip S Yu 2010. Semi-supervised feature selection for graph classification KDD '12. 793--802. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Ravi Kumar, Jasmine Novak, and Andrew Tomkins. 2006. Structure and Evolution of Online Social Networks. KDD '06. ACM, 611--617. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Jérôme Kunegis. 2013. Konect: the koblenz network collection. In WWW'13 companion. 1343--1350. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. F. Lazarsfeld and R. K. Merton 1954. Friendship as a social process: A substantive and methodological analysis. Freedom and control in modern society, New York: Van Nostrand (1954), 8--66.Google ScholarGoogle Scholar
  20. Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins 2008. Microscopic evolution of social networks. In KDD '08. 462--470. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Jure Leskovec, Deepayan Chakrabarti, Jon Kleinberg, Christos Faloutsos, and Zoubin Ghahramani. 2010. Kronecker graphs: An approach to modeling networks. JMLR, Vol. 11, Feb (2010), 985--1042.Google ScholarGoogle Scholar
  22. Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005. Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations KDD '05. 177--187. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Jure Leskovec, Kevin J Lang, Anirban Dasgupta, and Michael W Mahoney 2008. Statistical properties of community structure in large social and information networks WWW '08. ACM, 695--704.Google ScholarGoogle Scholar
  24. David Liben-Nowell and Jon M. Kleinberg 2007. The link-prediction problem for social networks. JASIST, Vol. 58, 7 (2007), 1019--1031. Google ScholarGoogle ScholarCross RefCross Ref
  25. Hao Ma 2014. On measuring social friend interest similarities in recommender systems SIGIR '14. ACM, 465--474.Google ScholarGoogle Scholar
  26. Peter V Marsden and Karen E Campbell 1984. Measuring tie strength. Social forces, Vol. 63, 2 (1984), 482--501. Google ScholarGoogle ScholarCross RefCross Ref
  27. M. McPherson, L. Smith-Lovin, and J.M. Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology (2001), 415--444. Google ScholarGoogle ScholarCross RefCross Ref
  28. R. Milo, S. Itzkovitz, N. Kashtan, R. Levitt, S. Shen-Orr, I. Ayzenshtat, M. Sheffer, and U. Alon. 2004. Superfamilies of evolved and designed networks. Science, Vol. 303, 5663 (March 2004), 1538--1542. Google ScholarGoogle ScholarCross RefCross Ref
  29. Alan Mislove, Massimiliano Marcon, Krishna P. Gummadi, Peter Druschel, and Bobby Bhattacharjee. 2007. Measurement and Analysis of Online Social Networks IMC'07. 29--42.Google ScholarGoogle Scholar
  30. Mark E. J. Newman. 2001. Clustering and preferential attachment in growing networks. Phys. Rev. E, Vol. 64, 2 (2001), 025102. Google ScholarGoogle ScholarCross RefCross Ref
  31. Mark E. J. Newman. 2006. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E Vol. 74 (2006), 036104. Issue 3.Google ScholarGoogle ScholarCross RefCross Ref
  32. Mark E. J. Newman, Steven H Strogatz, and Duncan J Watts. 2001. Random graphs with arbitrary degree distributions and their applications. Phys. Rev. E, Vol. 64, 2 (2001), 026118. Google ScholarGoogle ScholarCross RefCross Ref
  33. Ali Pinar, C. Seshadhri, and V. Vishal 2017. ESCAPE: Efficiently Counting All 5-Vertex Subgraphs WWW '17. 1431--1440.Google ScholarGoogle Scholar
  34. Pablo Robles, Sebastian Moreno, and Jennifer Neville. 2016. Sampling of Attributed Networks from Hierarchical Generative Models KDD '16. ACM, 1155--1164.Google ScholarGoogle Scholar
  35. Ryan A. Rossi and Nesreen K. Ahmed 2015. The Network Data Repository with Interactive Graph Analytics and Visualization AAAI' 15. 4292--4293. showURL%http://networkrepository.comGoogle ScholarGoogle ScholarDigital LibraryDigital Library
  36. Rok Sosic and Jure Leskovec 2015. Large Scale Network Analytics with SNAP. In WWW '15 Companion. ACM, 1537--1538. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su 2008. ArnetMiner: Extraction and Mining of Academic Social Networks KDD '08. 990--998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Johan Ugander, Lars Backstrom, and Jon Kleinberg. 2013. Subgraph Frequencies: Mapping the Empirical and Extremal Geography of Large Graph Collections WWW '13. 1307--1318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Johan Ugander, Lars Backstrom, Cameron Marlow, and Jon Kleinberg 2012. Structural diversity in social contagion. PNAS, Vol. 109, 16 (2012), 5962--5966. Google ScholarGoogle ScholarCross RefCross Ref
  40. Julian R Ullmann. 1976. An algorithm for subgraph isomorphism. J. ACM Vol. 23, 1 (1976), 31--42.Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Brian Uzzi. 1997. Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative science quarterly (1997), 35--67. Google ScholarGoogle ScholarCross RefCross Ref
  42. Joe H Ward Jr. 1963. Hierarchical grouping to optimize an objective function. Journal of the American statistical association, Vol. 58, 301 (1963), 236--244. Google ScholarGoogle ScholarCross RefCross Ref
  43. Duncan J. Watts and Steven H. Strogatz Jun 1998. Collective dynamics of small-world networks. Nature ( Jun 1998), 440--442.Google ScholarGoogle Scholar
  44. Rongjing Xiang, Jennifer Neville, and Monica Rogati. 2010. Modeling relationship strength in online social networks WWW '10. 981--990.Google ScholarGoogle Scholar
  45. R. Zafarani and H. Liu 2009. Social Computing Data Repository at ASU. (2009). showURL%http://socialcomputing.asu.eduendthebibliographyGoogle ScholarGoogle Scholar

Index Terms

  1. Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks

          Recommendations

          Comments

          Login options

          Check if you have access through your login credentials or your institution to get full access on this article.

          Sign in
          • Published in

            cover image ACM Conferences
            KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
            August 2017
            2240 pages
            ISBN:9781450348874
            DOI:10.1145/3097983

            Copyright © 2017 ACM

            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

            Publisher

            Association for Computing Machinery

            New York, NY, United States

            Publication History

            • Published: 4 August 2017

            Permissions

            Request permissions about this article.

            Request Permissions

            Check for updates

            Qualifiers

            • research-article

            Acceptance Rates

            KDD '17 Paper Acceptance Rate64of748submissions,9%Overall Acceptance Rate1,133of8,635submissions,13%

            Upcoming Conference

          PDF Format

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader