TY - GEN
T1 - The evolution of ego-centric triads
T2 - 2011 IEEE International Conference on Privacy, Security, Risk and Trust, PASSAT 2011 and 2011 IEEE International Conference on Social Computing, SocialCom 2011
AU - Doroud, Mina
AU - Bhattacharyya, Prantik
AU - Wu, S. Felix
AU - Felmlee, Diane
PY - 2011
Y1 - 2011
N2 - Scalability issues make it time-consuming to estimate even simple characteristics of large scale, online networks, and the constantly evolving qualities of these networks make it challenging to capture a representative picture of a particular networks properties. Here we focus on the evolution of all triads (ties between three nodes) in a graph, as a method of studying change over time in large scale, online social networks. For three month snapshots, we examine, and predict, transitions among all sixteen triad types (i.e., triad census) in a sample of three years of Facebook wall-post interactions. We introduce a new sampling approach for examining triads in online graphs, based on ego-centric networks of random seeds. We examine tendencies in the data toward properties related to balance theory, including structural balance, clusterability, ranked clusters, transitivity, hierarchical clusters, and the presence of "forbidden" triads. In a time series analysis, we successfully predict the evolution over time in the wall post network dataset, with relatively low levels of error. The findings demonstrate the utility of our ego- centric, two-step, random seed sampling approach for studying large scale networks and predicting macroscopic graph properties, as well as the advantages of examining transitions in the complete triad census for an online network.
AB - Scalability issues make it time-consuming to estimate even simple characteristics of large scale, online networks, and the constantly evolving qualities of these networks make it challenging to capture a representative picture of a particular networks properties. Here we focus on the evolution of all triads (ties between three nodes) in a graph, as a method of studying change over time in large scale, online social networks. For three month snapshots, we examine, and predict, transitions among all sixteen triad types (i.e., triad census) in a sample of three years of Facebook wall-post interactions. We introduce a new sampling approach for examining triads in online graphs, based on ego-centric networks of random seeds. We examine tendencies in the data toward properties related to balance theory, including structural balance, clusterability, ranked clusters, transitivity, hierarchical clusters, and the presence of "forbidden" triads. In a time series analysis, we successfully predict the evolution over time in the wall post network dataset, with relatively low levels of error. The findings demonstrate the utility of our ego- centric, two-step, random seed sampling approach for studying large scale networks and predicting macroscopic graph properties, as well as the advantages of examining transitions in the complete triad census for an online network.
UR - http://www.scopus.com/inward/record.url?scp=84862917835&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84862917835&partnerID=8YFLogxK
U2 - 10.1109/PASSAT/SocialCom.2011.101
DO - 10.1109/PASSAT/SocialCom.2011.101
M3 - Conference contribution
AN - SCOPUS:84862917835
SN - 9780769545783
T3 - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
SP - 172
EP - 179
BT - Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011
Y2 - 9 October 2011 through 11 October 2011
ER -