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Representation Learning on Graphs: Methods and Applications

Representation Learning on Graphs: Methods and ApplicationsWilliam L. of Computer ScienceStanford UniversityStanford, CA, 94305 AbstractMachine Learning on graphs is an important and ubiquitous task with Applications ranging from drugdesign to friendship recommendation in social networks. The primary challenge in this domain is findinga way to represent, or encode, graph structure so that it can be easily exploited by machine learningmodels. Traditionally, machine Learning approaches relied on user-defined heuristics to extract featuresencoding structural information about a graph ( , degree statistics or kernel functions). However,recent years have seen a surge in approaches that automatically learn to encode graph structure intolow-dimensional embeddings, using techniques based on deep Learning and nonlinear dimensionalityreduction.

The coloring in the left figure emphasizes differences in the nodes’ global positions in the graph: nodes have the same color if they belong to the same community, at a global level. In contrast, the coloring in the right figure denotes structural equivalence between nodes, or the fact that two nodes play

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Lagrangian Duality for Dummies - Stanford Computer Science

www-cs.stanford.edu

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ImageNet: A Large-Scale Hierarchical Image Database

www-cs.stanford.edu

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courses.cs.washington.edu

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courses.cs.washington.edu

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