Error Correcting Graph Matching: On the Influence of the Underlying Cost Function
IEEE Transactions on Pattern Analysis and Machine Intelligence
A graph distance metric combining maximum common subgraph and minimum common supergraph
Pattern Recognition Letters
Statistical phrase-based translation
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
Replicator Equations, Maximal Cliques, and Graph Isomorphism
Neural Computation
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Moses: open source toolkit for statistical machine translation
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
NOAM: news outlets analysis and monitoring system
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
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 develop a statistical methodology to validate the result of network inference algorithms, based on principles of statistical testing and machine learning. The comparison of results with reference networks, by means of similarity measures and null models, allows us to measure the significance of results, as well as their predictive power. The use of Generalised Linear Models allows us to explain the results in terms of available ground truth which we expect to be partially relevant. We present these methods for the case of inferring a network of News Outlets based on their preference of stories to cover. We compare three simple network inference methods and show how our technique can be used to choose between them. All the methods presented here can be directly applied to other domains where network inference is used.