Anonymous Walk Embeddings | ML with Graphs (Research Paper Walkthrough)

ICML 2020

Anonymous Walk Embeddings | ML with Graphs (Research Paper Walkthrough)

May 15, 2021
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#graphembedding #machinelearning #research The research talks about using Random Walk inspired Anonymous Walks as graph units to derive feature-based and data-driven graph embeddings. Watch to know more :) ⏩ Abstract: The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, anonymous walk, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs. Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 ⏩ OUTLINE: 0:00 - Abstract 01:06 - Anonymous Walks 05:56 - Rationale for Anonymous Walks 07:00 - AWE: Feature-based Model 11:54 - Sampling in AWE Feature-based Model 14:30 - AWE: data-driven Model ⏩ Paper Title: Anonymous Walk Embeddings ⏩ Paper: https://arxiv.org/pdf/1805.11921.pdf ⏩ Author: Sergey Ivanov, Evgeny Burnaev ⏩ Organisation: Skolkovo Institute of Science and Technology, Moscow, Russia | Criteo Research, Paris, France ⏩ IMPORTANT LINKS Full Playlist on Machine Learning with Graphs: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf Reconstructing Markov Processes from Independent and Anonymous Experiments: http://people.csail.mit.edu/zeyuan/paper/2014-DAM.pdf ********************************************* ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ Blog - https://prakhartechviz.blogspot.com ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Please feel free to share out the content and subscribe to my channel :) ⏩ Subscribe - https://youtube.com/channel/UCoz8NrwgL7U9535VNc0mRPA?sub_confirmation=1 Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #ml_with_graphs #representation #learning

0:00 Abstract 01:06 Anonymous Walks 05:56 Rationale for Anonymous Walks 07:00 AWE: Feature-based Model 11:54 Sampling in AWE Feature-based Model 14:30 AWE: data-driven Model
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