Improved performance of the greedy algorithm for partial cover
Information Processing Letters
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Meme-tracking and the dynamics of the news cycle
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Refining causality: who copied from whom?
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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We present a method for the reconstruction of networks, based on the order of nodes visited by a stochastic branching process. Our algorithm reconstructs a network of minimal size that ensures consistency with the data. Crucially, we show that global consistency with the data can be achieved through purely local considerations, inferring the neighbourhood of each node in turn. The optimisation problem solved for each individual node can be reduced to a set covering problem, which is known to be NP-hard but can be approximated well in practice. We then extend our approach to account for noisy data, based on the Minimum Description Length principle. We demonstrate our algorithms on synthetic data, generated by an SIR-like epidemiological model.