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

#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.

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⏩ 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: arxiv.org/abs/1706.02216v4
⏩ Author: William L. Hamilton, Rex Ying, Jure Leskovec
⏩ Organisation: Stanford

Graph Machine Learning Playlist:    • DEEPWALK: Online Learning of Social R...  

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About Me:
I am Prakhar Mishra and this channel is my passion project. I am currently pursuing my MS (by research) in Data Science. I have an industry work-ex of 3 years in the field of Data Science and Machine Learning with a particular focus on Natural Language Processing (NLP).