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
What is Twitter, a social network or a news media?
Proceedings of the 19th international conference on World wide web
Inferring networks of diffusion and influence
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Reconstruction of causal networks by set covering
ICANNGA'11 Proceedings of the 10th international conference on Adaptive and natural computing algorithms - Volume Part II
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 the NetCover algorithm, 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, and we verify performance on both synthetic and real-world data. We show that, crucially, the neighbourhood of each node may be inferred in turn, with global consistency between network and data achieved through purely local considerations. The resulting optimisation problem for each node can be reduced to a set covering problem, which though NP-hard can be approximated well in practice. We provide theoretical bounds on the performance of the algorithm, before describing an extension to account for noisy data, based on the Minimum Description Length principle. We first demonstrate the utility of our algorithm on synthetic data, generated by an SIR-like epidemiological model. Finally we test our approach on data gathered from the social networking site Twitter, demonstrating that we can extract the underlying social graph by analysing only the content of individual user feeds.