Small worlds: the dynamics of networks between order and randomness
Small worlds: the dynamics of networks between order and randomness
The "DGX" distribution for mining massive, skewed data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Identifying link farm spam pages
WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Neighborhood Formation and Anomaly Detection in Bipartite Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
On nash equilibria for a network creation game
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
An algorithm for reporting maximal c-cliques
Theoretical Computer Science
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
A Parallel Algorithm for Enumerating All Maximal Cliques in Complex Network
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
The price of anarchy in network creation games
Proceedings of the twenty-sixth annual ACM symposium on Principles of distributed computing
A network formation game for bipartite exchange economies
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Microscopic evolution of social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted graphs and disconnected components: patterns and a generator
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trusting spam reporters: A reporter-based reputation system for email filtering
ACM Transactions on Information Systems (TOIS)
Tutorial on recent progress in collaborative filtering
Proceedings of the 2008 ACM conference on Recommender systems
ACM Computing Surveys (CSUR)
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Analysis of large multi-modal social networks: patterns and a generator
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Surprising patterns for the call duration distribution of mobile phone users
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Perceptions of visualizing personal mobile communication patterns
Proceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia
Role defining using behavior-based clustering in telecommunication network
Expert Systems with Applications: An International Journal
An agent-based model for simultaneous phone and SMS traffic over time
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
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Given a real, and weighted person-to-person network which changes over time, what can we say about the cliques that it contains? Do the incidents of communication, or weights on the edges of a clique follow any pattern? Real, and in-person social networks have many more triangles than chance would dictate. As it turns out, there are many more cliques than one would expect, in surprising patterns. In this paper, we study massive real-world social networks formed by direct contacts among people through various personal communication services, such as Phone-Call, SMS, IM etc. The contributions are the following: (a) we discover surprising patterns with the cliques, (b) we report power-laws of the weights on the edges of cliques, (c) our real networks follow these patterns such that we can trust them to spot outliers and finally, (d) we propose the first utility-driven graph generator for weighted time-evolving networks, which match the observed patterns. Our study focused on three large datasets, each of which is a different type of communication service, with over one million records, and spans several months of activity.