Given a graph, how can we extract good features for the nodes? For example, given two large graphs from the same domain, how can we use information in one to do classification in the other (i.e., perform across-network classification or transfer learning on graphs)? Also, if one of the graphs is anonymized, how can we use information in one to de-anonymize the other? The key step in all such graph mining tasks is to find effective node features. We propose ReFeX (Recursive Feature eXtraction), a novel algorithm, that recursively combines local (node-based) features with neighborhood (egonet-based) features; and outputs regional features – capturing “behavioral ” information. We demonstrate how these powerful regional features can be used in within-network and across-network classification and de-anonymization tasks – without relying on homophily, or the availability of class labels. The contributions of our work are as follows: (a) ReFeX is scalable and (b) it is effective, capturing regional (“behavioral”) information in large graphs. We report experiments on real graphs from various domains with over 1M edges, where ReFeX outperforms its competitors on typical graph mining tasks like network classification and de-anonymization.

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Conditional random fields and other graphical models have achieved state of the art results in a variety of NLP and IE tasks including coreference and relation extraction. Increasingly, practitioners are using models with more complex structure—higher tree-width, larger fanout, more features, and more data—rendering even approximate inference methods such as MCMC inefficient. In this paper we propose an alternative MCMC sampling scheme in which transition probabilities are approximated by sampling from the set of relevant factors. We demonstrate that our method converges more quickly than a traditional MCMC sampler for both marginal and MAP inference. In an author coreference task with over 5 million mentions, we achieve a 13 times speedup over regular MCMC inference. 1

Conditional random fields and other graphical models have achieved state of the art results in a variety of tasks such as coreference, relation extraction, data integration, and parsing. Increasingly, practitioners are using models with more complex structure—higher treewidth, larger fan-out, more features, and more data—rendering even approximate inference methods such as MCMC inefficient. In this paper we propose an alternative MCMC sampling scheme in which transition probabilities are approximated by sampling from the set of relevant factors. We demonstrate that our method converges more quickly than a traditional MCMC sampler for both marginal and MAP inference. In an author coreference task with over 5 million mentions, we achieve a 13 times speedup over regular MCMC inference. 1

Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. We describe several challenges and our solutions in designing, implementing, and deploying Elementary. In particular, we first describe the conceptual framework and architecture of Elementary, and then discuss how we address scalability challenges to enable Web-scale deployment. First, to take advantage of diverse data resources and proven techniques, Elementary employs Markov logic, a succinct yet expressive language to specify probabilistic graphical models. Elementary accepts both domain-knowledge rules and classical machine-learning models such as conditional random fields, thereby integrating different data resources and KBC techniques in a principled manner. Second, to support large-scale KBC with terabytes of data and millions of entities, Elementary