a synthetic data generator for online social network graphs

A SYNTHETIC DATA GENERATOR FOR ONLINE SOCIAL NETWORK GRAPHS SOFTWARE
A SYNTHETIC DATA GENERATOR FOR ONLINE SOCIAL NETWORK GRAPHS SERIES

describes our community detection algorithm, GPSODM In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on (pp 175–182). Modeling information diffusion and community membership using stochastic optimization. Hajibagheri A, Hamzeh A, Sukthankar G (2013).

High productivity software for complex networks. Hagberg A, Schult D, Swart, P, Conway D, Séguin-Charbonneau L, Ellison C, Edwards B, Torrents J (2004) Networkx. Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Behav Brain Sci 16(4):681–735ĮU’s Data Protection Directive (2015) Justice, Protection of personal data. J Exp Psychol Gen 145(3):366–375ĭunbar RIM (1993) Coevolution of neocortical size, group size and language in humans. Commun ACM 51(1):107–113ĭehghani M, Johnson K, Hoover J, Sagi E, Garten J, Parmar NJ, Vaisey S, Iliev R, Graham J (2016) Purity homophily in social networks. 24, 2013ĭean J, Sanjay G (2008) MapReduce: simplified data processing on large clusters. A Simple Model of Homophily in Social Networks (2013) University Ca’ Foscari of Venice, Dept. SIAM, Philadelphia, PAĬurrarini S, Redondoy FV. on Weblogs and Social Media (ICWSM), vol 10, pp 10–17Ĭhakrabarti D, Zhan Y, Faloutsos C (2004) R-mat: A recursive model for graph mining. Issue 3 Ĭha M, Haddadi H, Benevenuto F, Gummadi KP (2010) Measuring User Influence in Twitter: The Million Follower Fallacy. J Stat Mech P10008īoncz P, Perez M, Gavalda R., Angles R, Erling O, Gubichev A, Spasić M, Pham MD, Martínez N (2014) Benchmark Design for Navigational Pattern Matching Benchmarking. Netw Sci (Camb Univ Press) 2(2):189–212īlondel VD, Guillaume JL, Lambiotte R, Lefebure E (2008) Fast unfolding of communities in large networks. Int AAAI Conf Weblogs Soc Media ICWSM 8(2009):361–362īlock P, Grund T (2014) Multidimensional homophily in friendship networks. In: Proceedings of the 2009 Winter Simulation Conference, 13–, pp 1003–1014īastian M, Heymann S, Jacomy M (2009) Gephi: an open source software for exploring and manipulating networks. In: Proceedings of SocInfo 2014īarrett CL, Beckman RJ, Khan M, Kumar VSA, Marathe MV, Stretz PE, Dutta T, Lewis B (2009) Generation and Analysis of Large Synthetic Social Contact Networks. FloridaĪli AM, Alvari H, Hajibagheri A, Lakkaraj K, Sukthankar G (2014) Synthetic generators for cloning social network data. The data generator is also highly configurable, with a sophisticated control parameter set for different “similarity/diversity” levels.Īli AM (2014) Synthetic generators for simulating social networks, 2014. A good match is obtained between the generated data and the target profiles and distributions, which is competitive with other state of the art methods. The empirical tests confirm that our approach generates a dataset which is both diverse and with a good fit to the target requirements, with a realistic modeling of noise and fitting to communities. In the following work, we present and validate an approach for populating a graph topology with synthetic data which approximates an online social network.

However, this presents a series of challenges related to generating a realistic dataset in terms of topologies, attribute values, communities, data distributions, correlations and so on. One possible solution to both of these problems is to use synthetically generated data. Two of the difficulties for data analysts of online social networks are (1) the public availability of data and (2) respecting the privacy of the users.

A SYNTHETIC DATA GENERATOR FOR ONLINE SOCIAL NETWORK GRAPHS SOFTWARE

A SYNTHETIC DATA GENERATOR FOR ONLINE SOCIAL NETWORK GRAPHS SERIES

A synthetic data generator for online social network graphs