PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
The "DGX" distribution for mining massive, skewed data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Heuristically Optimized Trade-Offs: A New Paradigm for Power Laws in the Internet
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Stochastic models for the Web graph
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Power laws and the AS-level internet topology
IEEE/ACM Transactions on Networking (TON)
Graphs over time: densification laws, shrinking diameters and possible explanations
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Epidemic thresholds in real networks
ACM Transactions on Information and System Security (TISSEC)
Flooding time in edge-Markovian dynamic graphs
Proceedings of the twenty-seventh ACM symposium on Principles of distributed computing
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
Fast Counting of Triangles in Large Real Networks without Counting: Algorithms and Laws
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Dynamical Processes on Complex Networks
Dynamical Processes on Complex Networks
On the evolution of user interaction in Facebook
Proceedings of the 2nd ACM workshop on Online social networks
RTG: A Recursive Realistic Graph Generator Using Random Typing
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
A Survey of Statistical Network Models
Foundations and Trends® in Machine Learning
Virus propagation on time-varying networks: theory and immunization algorithms
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Scalable discovery of best clusters on large graphs
Proceedings of the VLDB Endowment
Dynamic networks: models and algorithms
ACM SIGACT News
Content-based Modeling and Prediction of Information Dissemination
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Realistic, mathematically tractable graph generation and evolution, using kronecker multiplication
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
A temporal network analysis reveals the unprofitability of arbitrage in The Prosper Marketplace
Expert Systems with Applications: An International Journal
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A wealth of graph data, from email and telephone graphs to Twitter networks, falls into the category of dynamic "event" networks. Edges in these networks represent brief events, and their analysis leads to multiple interesting and important topics, such as the prediction of road traffic or modeling of communication flow. In this paper, we analyze a novel new dynamic event graph property, the "Dynamic Reachability Set" (DRS), which characterizes reachability within graphs across time. We discover that DRS histograms of multiple real world dynamic event networks follow novel distribution patterns. From these patterns, we introduce a new generative dynamic graph model, DRS-Gen. DRS-Gen captures the dynamic graph properties of connectivity and reachability, as well as generates time values for its edges. To the best of our knowledge, DRS-Gen is the first such model which produces exact time values on edges, allowing us to understand simultaneity across multiple information flows.