MapReduce: simplified data processing on large clusters
OSDI'04 Proceedings of the 6th conference on Symposium on Opearting Systems Design & Implementation - Volume 6
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
PSCAN: A Parallel Structural Clustering Algorithm for Big Networks in MapReduce
AINA '13 Proceedings of the 2013 IEEE 27th International Conference on Advanced Information Networking and Applications
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Substantial percent of global Internet users are now actively use Twitter. In recent times, Twitter has been experiencing explosive growth, attracting celebrities consequently a growing mass of user coverage. Insights of such a social network aid researchers in understanding behavioral dynamics of the society. Though there have been attempts to study social networks, they did not scale to process social networks on the scale of Twitter user-follower network. In this paper we uncovered some of the essential properties of the complete Twitter user-follower network. The properties include degree distribution, connectivity, strength of following relationships, clustering coefficient. Our investigations showed that the Twitter user-follower network follows power-law degree distribution. We found Twitter being a connected network. The strength of the relationships among users is distributed nearly uniform on the scale of 0.0 to 1.0. Nearly 90% of the users possess '0' clustering coefficient, which refers to the least possibility to find communities in the network. In addition to the listed properties, this study found communities of users with high clustering coefficient despite many users with low clustering coefficient. A sample of the communities is validated manually for accuracy. The validation proved that the communities are representing users of similar interests. The communities found from this work yields to friend recommendations, target based advertisements, etc.