Cited By
View all- Citraro SRossetti G(2021)X-Mark: a benchmark for node-attributed community discovery algorithmsSocial Network Analysis and Mining10.1007/s13278-021-00823-211:1Online publication date: 15-Oct-2021
Growing Attributed Networks through Local Processes
Abstract
This paper proposes an attributed network growth model. Despite the knowledge that individuals use limited resources to form connections to similar others, we lack an understanding of how local and resource-constrained mechanisms explain the emergence of structural properties found in real-world networks. We make three contributions. First, we propose a simple and accurate model of attributed network growth that jointly explains the emergence of in-degree, local clustering, clustering-degree relationship and attribute mixing patterns. Second, we make use of biased random walks to develop a model that forms edges locally, without recourse to global information. Third, we account for multiple sociological phenomena: bounded rationality; structural constraints; triadic closure; attribute homophily; preferential attachment. Our experiments show that the proposed Attributed Random Walk (ARW) model accurately preserves network structure and attribute mixing patterns of real-world networks; it improves upon the performance of eight well-known models by a significant margin of 2.5-10 × .
References
[1]
{n. d.}. APS Datasets for Research. ({n. d.}). https://journals.aps.org/datasets
[2]
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, Rodney Kinney, Sebastian Kohlmeier, Kyle Lo, Tyler Murray, Hsu-Han Ooi, Matthew Peters, Joanna Power, Sam Skjonsberg, Lucy Lu Wang, Chris Wilhelm, Zheng Yuan, Madeleine van Zuylen, and Oren Etzioni. 2018. Construction of the Literature Graph in Semantic Scholar. In NAACL.
[3]
Albert-László Barabási and Re´ka Albert. 1999. Emergence of scaling in random networks. science 286, 5439 (1999), 509-512.
[4]
Michael Bell, Supun Perera, Mahendrarajah Piraveenan, Michiel Bliemer, Tanya Latty, and Chris Reid. 2017. Network growth models: A behavioural basis for attachment proportional to fitness. Scientific Reports 7(2017), 42431.
[5]
Ginestra Bianconi and Albert-László Barabási. 2001. Bose-Einstein condensation in complex networks. Physical review letters 86, 24 (2001), 5632.
[6]
Per Block and Thomas Grund. 2014. Multidimensional homophily in friendship networks. Network Science 2, 2 (2014), 189-212.
[7]
Avrim Blum, TH Hubert Chan, and Mugizi Robert Rwebangira. 2006. A random-surfer web-graph model. In 2006 Proceedings of the Third Workshop on Analytic Algorithmics and Combinatorics (ANALCO). SIAM, 238-246.
[8]
Be´la Bollobás and Oliver Riordan. 2004. The diameter of a scale-free random graph. Combinatorica 24, 1 (2004), 5-34.
[9]
Anna D Broido and Aaron Clauset. 2018. Scale-free networks are rare. arXiv preprint arXiv:1801.03400(2018).
[10]
Guido Caldarelli, Andrea Capocci, Paolo De Los Rios, and Miguel A Munoz. 2002. Scale-free networks from varying vertex intrinsic fitness. Physical review letters 89, 25 (2002), 258702.
[11]
Prasad Chebolu and Páll Melsted. 2008. PageRank and the random surfer model. In Proceedings of the nineteenth annual ACM-SIAM symposium on Discrete algorithms. Society for Industrial and Applied Mathematics, 1010-1018.
[12]
Maurício L de Almeida, Gabriel A Mendes, G Madras Viswanathan, and Luciano R da Silva. 2013. Scale-free homophilic network. The European Physical Journal B 86, 2 (2013), 38.
[13]
Sergey N Dorogovtsev, Jose Ferreira F Mendes, and Alexander N Samukhin. 2000. Structure of Growing Networks: Exact Solution of the Barabási-Albert's Model. arXiv preprint cond-mat/0004434(2000).
[14]
James H Fowler and Sangick Jeon. 2008. The authority of Supreme Court precedent. Social networks 30, 1 (2008), 16-30.
[15]
Johannes Gehrke, Paul Ginsparg, and Jon Kleinberg. 2003. Overview of the 2003 KDD Cup. ACM SIGKDD Explorations Newsletter 5, 2 (2003), 149-151.
[16]
Gerd Gigerenzer and Daniel G Goldstein. 1996. Reasoning the fast and frugal way: models of bounded rationality.Psychological review 103, 4 (1996), 650.
[17]
Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, and Dawn Song. 2012. Evolution of social-attribute networks: measurements, modeling, and implications using google+. In Proceedings of the 2012 Internet Measurement Conference. ACM, 131-144.
[18]
Phillip Good. 2013. Permutation tests: a practical guide to resampling methods for testing hypotheses. Springer Science & Business Media.
[19]
Carlos Herrera and Pedro J Zufiria. 2011. Generating scale-free networks with adjustable clustering coefficient via random walks. In Network Science Workshop (NSW), 2011 IEEE. IEEE, 167-172.
[20]
Petter Holme and Beom Jun Kim. 2002. Growing scale-free networks with tunable clustering. Physical review E 65, 2 (2002), 026107.
[21]
Fariba Karimi, Mathieu Ge´nois, Claudia Wagner, Philipp Singer, and Markus Strohmaier. 2017. Visibility of minorities in social networks. arXiv preprint arXiv:1702.00150(2017).
[22]
Kibae Kim and Jörn Altmann. 2017. Effect of homophily on network formation. Communications in Nonlinear Science and Numerical Simulation 44 (2017), 482-494.
[23]
Konstantin Klemm and Victor M Eguiluz. 2002. Highly clustered scale-free networks. Physical Review E 65, 3 (2002), 036123.
[24]
Gueorgi Kossinets and Duncan J. Watts. 2009. Origins of Homophily in an Evolving Social Network. Amer. J. Sociology 115(2009), 405-450.
[25]
Ravi Kumar, Prabhakar Raghavan, Sridhar Rajagopalan, D Sivakumar, Andrew Tomkins, and Eli Upfal. 2000. Stochastic models for the web graph. In Foundations of Computer Science, 2000. Proceedings. 41st Annual Symposium on. IEEE, 57-65.
[26]
Jure Leskovec, Lars Backstrom, Ravi Kumar, and Andrew Tomkins. 2008. Microscopic evolution of social networks. In Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 462-470.
[27]
Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. 2005. Graphs over time: densification laws, shrinking diameters and possible explanations. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining. ACM, 177-187.
[28]
Barton L Lipman. 1995. Information processing and bounded rationality: a survey. Canadian Journal of Economics(1995), 42-67.
[29]
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology 27, 1 (2001), 415-444.
[30]
Matúš Medo, Giulio Cimini, and Stanislao Gualdi. 2011. Temporal effects in the growth of networks. Physical review letters 107, 23 (2011), 238701.
[31]
Abbas Mehrabian and Nick Wormald. 2016. Its a small world for random surfers. Algorithmica 76, 2 (2016), 344-380.
[32]
Stefano Mossa, Marc Barthelemy, H Eugene Stanley, and Luis A Nunes Amaral. 2002. Truncation of power law behavior in scale-free network models due to information filtering. Physical Review Letters 88, 13 (2002), 138701.
[33]
Mark Newman. 2010. Networks: an introduction. Oxford university press.
[34]
Mark EJ Newman. 2001. Clustering and preferential attachment in growing networks. Physical review E 64, 2 (2001), 025102.
[35]
Mark EJ Newman. 2002. Assortative mixing in networks. Physical review letters 89, 20 (2002), 208701.
[36]
Dragomir R. Radev, Pradeep Muthukrishnan, Vahed Qazvinian, and Amjad Abu-Jbara. 2013. The ACL anthology network corpus. Language Resources and Evaluation(2013), 1-26.
[37]
Jari Saramäki and Kimmo Kaski. 2004. Scale-free networks generated by random walkers. Physica A: Statistical Mechanics and its Applications 341 (2004), 80-86.
[38]
Harshay Shah, Suhansanu Kumar, and Hari Sundaram. 2017. Growing Attributed Networks through Local Processes. arXiv preprint arXiv:1712.10195(2017).
[39]
Georg Simmel. 1950. The sociology of georg simmel. Vol. 92892. Simon and Schuster.
[40]
Herbert A Simon. 1972. Theories of bounded rationality. Decision and organization 1, 1 (1972), 161-176.
[41]
Mayank Singh, Rajdeep Sarkar, Pawan Goyal, Animesh Mukherjee, and Soumen Chakrabarti. 2017. Relay-linking models for prominence and obsolescence in evolving networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 1077-1086.
[42]
Alexei Vazquez. 2000. Knowing a network by walking on it: emergence of scaling. arXiv preprint cond-mat/0006132(2000).
[43]
Dashun Wang, Chaoming Song, and Albert-László Barabási. 2013. Quantifying long-term scientific impact. Science 342, 6154 (2013), 127-132.
[44]
Li-Na Wang, Jin-Li Guo, Han-Xin Yang, and Tao Zhou. 2009. Local preferential attachment model for hierarchical networks. Physica A: Statistical Mechanics and its Applications 388, 8(2009), 1713-1720.
[45]
Jianyang Zeng, Wen-Jing Hsu, and Suiping Zhou. 2005. Construction of scale-free networks with partial information. Lecture notes in computer science 3595 (2005), 146.
[46]
Elena Zheleva, Hossam Sharara, and Lise Getoor. 2009. Co-evolution of social and affiliation networks. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 1007-1016.
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Published: 13 May 2019
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View all- Citraro SRossetti G(2021)X-Mark: a benchmark for node-attributed community discovery algorithmsSocial Network Analysis and Mining10.1007/s13278-021-00823-211:1Online publication date: 15-Oct-2021
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