GAT: Graph Attention Networks (Graph ML Research Paper Walkthrough)

#attention #graphml #machinelearning
⏩ Abstract: We present graph attention networks (GATs), novel neural network architectures that operate on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods' features, we enable (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront. In this way, we address several key challenges of spectral-based graph neural networks simultaneously and make our model readily applicable to inductive as well as transductive problems. Our GAT models have achieved state-of-the-art results across three established transductive and inductive graph benchmarks: the Cora and Citeseer citation network datasets, as well as a protein-protein interaction dataset (wherein test graphs are entirely unseen during training).

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⏩ OUTLINE:
0:00 - Abstract and Background
02:37 - Understanding Graph Attention Layer - Theory, Equations

⏩ Paper Title: Graph Attention Networks
⏩ Paper: https://arxiv.org/abs/1710.10903v1
⏩ Author: Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio
⏩ Organisation: University of Cambridge, UAB, Montreal Institute for Learning Algorithms

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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).

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