Learning SteadyStates of Iterative Algorithms over Graphs
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Proceedings of the 35th International Conference on Machine Learning, PMLR 80:11141122, 2018.
Abstract
Many graph analytics problems can be solved via iterative algorithms where the solutions are often characterized by a set of steadystate conditions. Different algorithms respect to different set of fixed point constraints, so instead of using these traditional algorithms, can we learn an algorithm which can obtain the same steadystate solutions automatically from examples, in an effective and scalable way? How to represent the meta learner for such algorithm and how to carry out the learning? In this paper, we propose an embedding representation for iterative algorithms over graphs, and design a learning method which alternates between updating the embeddings and projecting them onto the steadystate constraints. We demonstrate the effectiveness of our framework using a few commonly used graph algorithms, and show that in some cases, the learned algorithm can handle graphs with more than 100,000,000 nodes in a single machine.
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