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Estimating Clustering Coefficients and Size of Social Networks via Random Walk

Liran Katzir, Stephen J. Hardiman
2015 ACM Transactions on the Web  
Online social networks have become a major force in today's society and economy. The largest of today's social networks may have hundreds of millions to more than a billion users.  ...  One such task is computing the clustering coefficient of a network. Another task is to compute the network * Research was conducted while the author was unaffiliated.  ...  on the accuracy of previous network average clustering coefficient estimation algorithms; and (c) an improved external access network size estimation algorithm.  ... 
doi:10.1145/2790304 fatcat:5w3wwkxbcfecrlizm442mhzsr4

Estimating clustering coefficients and size of social networks via random walk

Stephen J. Hardiman, Liran Katzir
2013 Proceedings of the 22nd international conference on World Wide Web - WWW '13  
To estimate the clustering coefficient, the connectivity of each node in the random walk sequence is tested in turn.  ...  on the accuracy of previous network average clustering coefficient estimation algorithms; and (c) an improved external access network size estimation algorithm.  ...  on the accuracy of previous network average clustering coefficient estimation algorithms; and (c) an improved external access network size estimation algorithm.  ... 
doi:10.1145/2488388.2488436 dblp:conf/www/HardimanK13 fatcat:eczeici7urhu3fqqrsvtlwy6uy

Estimating High Betweenness Centrality Nodes via Random walk in Social Networks

Kazuki Nakajima, Kazuyuki Shudo
2020 Journal of Information Processing  
The proposed algorithm firstly obtains a small sample that includes many of top-k nodes with the highest betweenness centrality via a random walk on a social network.  ...  However, all the graph data of real social networks are not typically available to third parties such as researchers or marketers, and hence, an estimation algorithm based on sampling the graph data is  ...  Acknowledgments This work was supported by New Energy and Industrial Technology Development Organization (NEDO).  ... 
doi:10.2197/ipsjjip.28.436 fatcat:ifsumo5zvrb3hljfzxcnofirry

Model for Generating Scale-Free Artificial Social Networks Using Small-World Networks

Farhan Amin, Gyu Sang Choi
2022 Computers Materials & Continua  
We present a network growth model in this paper and build a scale-free artificial social network with controllable clustering coefficients.  ...  The motivation of this study is to understand and capture the clustering properties of large networks and social networks.  ...  Acknowledgement: We thank our families and colleagues who provided us with moral support. Conflicts of Interest: The authors declare they have no conflicts of interest regarding the present study.  ... 
doi:10.32604/cmc.2022.029927 fatcat:nqr2vykiqbckvkd5av76islssq

New Survey Questions and Estimators for Network Clustering with Respondent-driven Sampling Data

Ashton M. Verdery, Jacob C. Fisher, Nalyn Siripong, Kahina Abdesselam, Shawn Bauldry
2017 Sociological methodology  
We find that clustering coefficient estimators retain desirable properties in RDS samples.  ...  We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic  ...  Giovanna Merli, Ann Jolly, and Anne DeLessio-Parson for providing information about aspects of the empirical cases we examine.  ... 
doi:10.1177/0081175017716489 pmid:30337767 pmcid:PMC6191199 fatcat:stdi6ggaineh3noykkqb72hyd4

New Survey Questions and Estimators for Network Clustering with Respondent-Driven Sampling Data [article]

Ashton M. Verdery, Jacob C. Fisher, Nalyn Siripong, Kahina Abdesselam, Shawn Bauldry
2016 arXiv   pre-print
We find that clustering coefficient estimators retain desirable properties in RDS samples.  ...  We use simulations to explore how these estimators, originally developed for random walk samples of computer networks, perform when applied to RDS samples with characteristics encountered in realistic  ...  Giovanna Merli, Ann Jolly, and Anne DeLessio-Parson for providing information about aspects of the empirical cases we examine.  ... 
arXiv:1610.06683v1 fatcat:o7a3fy4y5jg2flpvbw7g7w3pxe

Ensuring Reliable Monte Carlo Estimates of Network Properties [article]

Haema Nilakanta, Zack W. Almquist, Galin L. Jones
2019 arXiv   pre-print
In particular, with respect to two random-walk algorithms, a simple random walk and a Metropolis-Hastings random walk, we construct and compare network parameter estimates, effective sample sizes, coverage  ...  The literature in social network analysis has largely focused on methods and models which require complete network data; however there exist many networks which can only be studied via sampling methods  ...  We estimated the mean degree and mean clustering coefficient. Implementation We ran 100 chains of length 100,000 from random starting nodes.  ... 
arXiv:1911.08682v2 fatcat:n6zraynounhrddkoaoh6cyu7ra

Sampling social networks using shortest paths

Alireza Rezvanian, Mohammad Reza Meybodi
2015 Physica A: Statistical Mechanics and its Applications  
Experimental results show that the proposed sampling method outperforms the existing method such as random edge sampling, random node sampling, random walk sampling and Metropolis-Hastings random walk  ...  Due to the large scale and access limitations (e.g., privacy policies) of online social network services such as Facebook and Twitter, it is difficult to access the whole public network in a limited amount  ...  Related works There are a limited number of recent researches on studying, characterizing and estimating the properties of online social networks via sampling.  ... 
doi:10.1016/j.physa.2015.01.030 fatcat:myd2wjt7vnedzk2piicmathrzy

Performance Analysis and Comparison of Sampling Algorithms in Online Social Network

Stuti K., Atul Srivastava
2016 International Journal of Computer Applications  
Here, the considered parameters are node-degree distribution and clustering coefficient which effect the performance of an algorithm in generating unbiased samples.  ...  Random walk graph sampling has been considered as a fundamental tool to collect uniform node samples from a large graph.  ...  For the evaluation of clustering coefficient as a parameter, we need to obtain the network average clustering coefficient (NACC).  ... 
doi:10.5120/ijca2016907868 fatcat:meznuvlrqbczfjfvxwk2enyjoq

A new learning automata-based sampling algorithm for social networks

Alireza Rezvanian, Mohammad Reza Meybodi
2015 International Journal of Communication Systems  
Due to the large data and privacy issues of social network services, there is only a limited local access to whole network data in a reasonable amount of time.  ...  Therefore, network sampling arises to studying the characterization of real networks such as communication, technological, information and social networks.  ...  BACKGROUND AND PRELIMINARIES Related work Since, a few number of recent works on studying, characterizing and estimating the properties of complex real networks such as online social networks (OSN) via  ... 
doi:10.1002/dac.3091 fatcat:d2nt4atpffhgpp5hh3jrtg67um

Infection in Social Networks: Using Network Analysis to Identify High-Risk Individuals

R. M. Christley, G. L. Pinchbeck, R. G. Bowers, D. Clancy, N. P. French, R. Bennett, J. Turner
2005 American Journal of Epidemiology  
Simulation studies using susceptible-infectious-recovered models were conducted to estimate individuals' risk of infection and time to infection in small-world and randomly mixing networks.  ...  Here, the authors use the centrality measures degree (number of contacts), random-walk betweenness (a measure of the proportion of times an individual lies on the path between other individuals), shortest-path  ...  Conflict of interest: none declared.  ... 
doi:10.1093/aje/kwi308 pmid:16177140 fatcat:udkwnoovjfedzkq5ecqjopsx74

A general framework for estimating graphlet statistics via random walk

Xiaowei Chen, Yongkun Li, Pinghui Wang, John C. S. Lui
2016 Proceedings of the VLDB Endowment  
In this work, we propose a general and novel framework to estimate graphlet statistics of "any size". Our framework is based on collecting samples through consecutive steps of random walks.  ...  We derive an analytical bound on the sample size (via the Chernoff-Hoeffding technique) to guarantee the convergence of our unbiased estimator.  ...  Graph sampling through crawling is widely used in this scenario to estimate graph properties such as degree distribution [17, 19, 11] , clustering coefficient [12] and size of graphs [15] .  ... 
doi:10.14778/3021924.3021940 fatcat:qk4tqxl2zzedrhaq6yspaznnem

Practical Recommendations on Crawling Online Social Networks

Minas Gjoka, Maciej Kurant, Carter T. Butts, Athina Markopoulou
2011 IEEE Journal on Selected Areas in Communications  
We show how these diagnostics can be used to effectively determine when a random walk sample is of adequate size and quality.  ...  Our goal in this paper is to develop a practical framework for obtaining a uniform sample of users in an online social network (OSN) by crawling its social graph.  ...  The clustering coefficient of a network is the average C over all nodes. We find the average clustering coefficient of Facebook to be C = 0.16, similar to that reported in [13] .  ... 
doi:10.1109/jsac.2011.111011 fatcat:sojnysvrhvbrnhm3nd4b2yqtte

Search Result Clustering via Randomized Partitioning of Query-Induced Subgraphs [article]

Aleksandar Bradic
2008 arXiv   pre-print
We define the notion of "query-induced subgraph" and formulate the problem of search result clustering as a problem of efficient partitioning of given subgraph into topic-related clusters.  ...  Finally, we present a practical clustering search engine developed as a part of this research and use it to get results about real-world performance of proposed concepts.  ...  Veljko Milutinovic, who mentored this research as a part of my diploma thesis at the faculty of Electrical Engineering, Belgrade.  ... 
arXiv:0811.4186v1 fatcat:afkmhct7nncyde6x2d26jj5xda

Estimating the size of online social networks

Shaozhi Ye, S. Felix Wu
2011 International Journal of Social Computing and Cyber-Physical Systems  
ACKNOWLEDGEMENTS The authors would like to thank Alan Mislove for sharing with us the social graphs and Matt Spear for crawling the Buzznet graph.  ...  The authors are also grateful to Jeff Rowe and Prantik Bhattacharyya for helpful references. APPENDIX PROOF OF THEOREM 2 Proof: Notice that  ...  As an important metric for small world graphs, the clustering coefficient is computed and analyzed in many social network studies [8] .  ... 
doi:10.1504/ijsccps.2011.044172 fatcat:fchfxrnrhzhbxmpkrtlqmmi73i
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