Stanford CS224W: Machine Learning with Graphs - Stanford CS224W: ML with Graphs | 2021 | Lecture 16.2 - Position-Aware Graph Neural Networks

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EpTPwM Jure Leskovec Computer Science, PhD We introduce the idea of Position-aware for graphs. To start, we define position-aware tasks, where we would like to classify nodes based on their positions in the graph. We demonstrate that certain position-aware tasks will always cause GNNs to fail. Our solution is the Position-aware Graph Neural Networks (P-GNN). The key idea of P-GNN is to introduce randomly selected anchors node, where we will embed all the nodes by computing their shortest path distances to these anchor nodes. To save the number of anchors needed, we further generalize the notion of anchor to anchor-sets, where each anchor-set contains a varied number of nodes. You can find more details on the P-GNN paper. https://arxiv.org/abs/1906.04817 To follow along with the course schedule and syllabus, visit: http://web.stanford.edu/class/cs224w/ #machinelearning #machinelearningcourse