Communications of the ACM
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Diffusion Kernels on Graphs and Other Discrete Input Spaces
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Iterative record linkage for cleaning and integration
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Extensions of marginalized graph kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Disambiguating Web appearances of people in a social network
WWW '05 Proceedings of the 14th international conference on World Wide Web
Protein function prediction via graph kernels
Bioinformatics
Shortest-Path Kernels on Graphs
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Graph kernels between point clouds
Proceedings of the 25th international conference on Machine learning
Differential privacy: a survey of results
TAMC'08 Proceedings of the 5th international conference on Theory and applications of models of computation
The Journal of Machine Learning Research
Large-scale collective entity matching
Proceedings of the VLDB Endowment
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
Weisfeiler-Lehman Graph Kernels
The Journal of Machine Learning Research
Collective context-aware topic models for entity disambiguation
Proceedings of the 21st international conference on World Wide Web
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Proceedings of the VLDB Endowment
LINDA: distributed web-of-data-scale entity matching
Proceedings of the 21st ACM international conference on Information and knowledge management
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This paper presents a novel method for entity disambiguation in anonymized graphs using local neighborhood structure. Most existing approaches leverage node information, which might not be available in several contexts due to privacy concerns, or information about the sources of the data. We consider this problem in the supervised setting where we are provided only with a base graph and a set of nodes labelled as ambiguous or unambiguous. We characterize the similarity between two nodes based on their local neighborhood structure using graph kernels; and solve the resulting classification task using SVMs. We give empirical evidence on two real-world datasets, comparing our approach to a state-of-the-art method, highlighting the advantages of our approach. We show that using less information, our method is significantly better in terms of either speed or accuracy or both. We also present extensions of two existing graphs kernels, namely, the direct product kernel and the shortest-path kernel, with significant improvements in accuracy. For the direct product kernel, our extension also provides significant computational benefits. Moreover, we design and implement the algorithms of our method to work in a distributed fashion using the GraphLab framework, ensuring high scalability.