The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Prediction and ranking algorithms for event-based network data
ACM SIGKDD Explorations Newsletter
Entity Resolution with Markov Logic
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Collective entity resolution in relational data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Structured entity identification and document categorization: two tasks with one joint model
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
A unified approach for schema matching, coreference and canonicalization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Foundations and Trends in Databases
Joint unsupervised coreference resolution with Markov logic
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Cautious inference in collective classification
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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There is a growing amount of observational data describing networks– examples include social networks, communication networks, and biological networks. As the amount of available data increases, so has our interest in analyzing these networks in order to uncover (1) general laws that govern their structure and evolution, and (2) patterns and predictive models to develop better policies and practices. However, a fundamental challenge in dealing with this newly available observational data describing networks is that the data is often of dubious quality–it is noisy and incomplete–and before any analysis method can be applied, the data must be cleaned, missing information inferred and mistakes corrected. Skipping this cleaning step can lead to flawed conclusions for things as simple as degree distribution and centrality measures; for more complex analytic queries, the results are even more likely to be inaccurate and misleading.