GraphSAGE: Inductive Representation Learning on Large Graphs (Graph ML Research Paper Walkthrough)

GraphSAGE: Inductive Representation Learning on Large Graphs (Graph ML Research Paper Walkthrough)

Sep 21, 2021
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#graphsage #machinelearning #graphml In this video, we go will through this popular GraphSAGE paper in the field of GNN and understand the inductive learning methodology on large graphs. ⏩ Abstract: Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in the graph are present during training of the embeddings; these previous approaches are inherently transductive and do not naturally generalize to unseen nodes. Here we present GraphSAGE, a general, inductive framework that leverages node feature information (e.g., text attributes) to efficiently generate node embeddings for previously unseen data. Instead of training individual embeddings for each node, we learn a function that generates embeddings by sampling and aggregating features from a node's local neighborhood. Our algorithm outperforms strong baselines on three inductive node-classification benchmarks: we classify the category of unseen nodes in evolving information graphs based on citation and Reddit post data, and we show that our algorithm generalizes to completely unseen graphs using a multi-graph dataset of protein-protein interactions. 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 and Introduction 01:00 - Visual Illustration of GraphSAGE 04:21 - Embedding Generation algorithm with GraphSAGE 08:00 - Learning Parameters of GraphSAGE 10:46 - Aggregator Architectures (Mean Aggr, LSTM Aggr, Pool Aggr) and Wrap-up ⏩ Paper Title: Inductive Representation Learning on Large Graphs ⏩ Paper: https://arxiv.org/abs/1706.02216v4 ⏩ Author: William L. Hamilton, Rex Ying, Jure Leskovec ⏩ Organisation: Stanford Graph Machine Learning Playlist: https://www.youtube.com/watch?v=-uJL_ANy1jc&list=PLsAqq9lZFOtU7tT6mDXX_fhv1R1-jGiYf ********************************************** If you want to support me financially which is totally optional and voluntary ❤️ You can consider buying me chai ( because I don't drink coffee :) ) at https://www.buymeacoffee.com/TechvizCoffee ❤️ Support using Paypal - https://www.paypal.com/paypalme/TechVizDataScience ********************************************** ⏩ Youtube - https://www.youtube.com/c/TechVizTheDataScienceGuy ⏩ LinkedIn - https://linkedin.com/in/prakhar21 ⏩ Medium - https://medium.com/@prakhar.mishra ⏩ GitHub - https://github.com/prakhar21 ⏩ Twitter - https://twitter.com/rattller ********************************************* Tools I use for making videos :) ⏩ iPad - https://tinyurl.com/y39p6pwc ⏩ Apple Pencil - https://tinyurl.com/y5rk8txn ⏩ GoodNotes - https://tinyurl.com/y627cfsa #techviz #datascienceguy #representation #research #graphs

0:00 - Abstract and Introduction 01:00 - Visual Illustration of GraphSAGE 04:21 - Embedding Generation algorithm with GraphSAGE 08:00 - Learning Parameters of GraphSAGE 10:46 - Aggregator Architectures (Mean Aggr, LSTM Aggr, Pool Aggr) and Wrap-up
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