A model for reasoning about persistence and causation
Computational Intelligence
Dynamic probabilistic relational models
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Labeling schemes for weighted dynamic trees
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
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A dynamic network is a special type of network which is comprised of connected transactors which have repeated evolving interaction. Data on large dynamic networks such as telecommunications networks and the Internet are pervasive. However, representing dynamic networks in a manner that is conducive to effcient large-scale analysis is a challenge. In this paper, we represent dynamic graphs using a data structure introduced by Cortes et. al. [3]. Our work improves on their heuristic arguments by formalizing the representation with three tunable parameters. In doing this, we develop a generic framework for evaluating and tuning any dynamic graph. We show that the storage saving approximations involved in the representation do not affect predictive performance, and typically improve it. We motivate our approach using a fraud detection example from the telecommunications industry, and demonstrate that we can outperform published results on the fraud detection task.