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Machine Learning on Graphs: A Model and Comprehensive Taxonomy

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Ines Chami: Working with graph-structured data? Check out our recent survey for Machine Learning on Graphs: https://arxiv.org/pdf/2005.03675.pdf We propose a simple framework (GraphEDM) and a comprehensive Taxonomy to review and unify several graph representation learning methods. https://t.co/T2hBCOYny8

12 replies, 573 likes


arxiv: Machine Learning on Graphs: A Model and Comprehensive Taxonomy. http://arxiv.org/abs/2005.03675 https://t.co/gFyMxKeXlF

0 replies, 39 likes


Bryan Perozzi: Our graph representation survey is up on arxiv (w/ @chamii22 @HazyResearch). My favorite part is the taxonomy of graph learning methods - try it next time you need to explain the burgeoning world of graph neural networks to a friend (or boss 😉)! #DeepLearning #MachineLearning

1 replies, 38 likes


MIT CSAIL: New framework (GraphEDM) and taxonomy aims to review and unify several graph representation learning methods. Read the full survey for machine learning on graphs: https://arxiv.org/pdf/2005.03675.pdf (v/@Stanford, @USC & @GoogleAI, h/t @chamii22) https://t.co/PQIy4CPlqh

0 replies, 33 likes


Kristian Kersting: Nice overview & conceptualization of (differentiable) approaches to learning on graphs. It is really important to get overviews & unifying views. 🙏 Follow up could be on learning with graphs, showing also the strong connection to graph kernels (via WL & neural fingerprints etc.)

1 replies, 19 likes


Underfox: Researchers have presented GraphEDM, a general framework which generalizes popular algorithms for semi-supervised learning on graphs and unsupervised learning of graph representations into a single consistent approach. @Google #MachineLearning https://arxiv.org/pdf/2005.03675.pdf https://t.co/iLewYMjBFi

0 replies, 3 likes


OGAWA, Tadashi: => "Machine Learning on Graphs: A Model and Comprehensive Taxonomy", arXiv, May 7, 2020 https://arxiv.org/abs/2005.03675 Graph Encoder Decoder Model, generalizes popular algorithms for semi-supervised learning on Graphs 127 ref TF implementation of GCNN models https://github.com/google/gcnn-survey-paper https://t.co/NaSs5lYvMr

0 replies, 1 likes


Brundage Bot: Machine Learning on Graphs: A Model and Comprehensive Taxonomy. Ines Chami, Sami Abu-El-Haija, Bryan Perozzi, Christopher Ré, and Kevin Murphy http://arxiv.org/abs/2005.03675

1 replies, 0 likes


Content

Found on May 11 2020 at https://arxiv.org/pdf/2005.03675.pdf

PDF content of a computer science paper: Machine Learning on Graphs: A Model and Comprehensive Taxonomy