Identity-aware Graph Neural Networks Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec Department of Computer Science, Stanford University fjiaxuan, jgs8, rexy, jureg@cs.stanford.edu Abstract Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. Runshort fixed-length random walks starting from each node on the graph using some strategy R 2. How- For each node #collect 6 7(#), the multiset* of nodes visited on random walks starting from u 3. Abstract

Graph convolutional neural networks (GCNs) embed nodes in a graph into Euclidean space, which has been shown to incur a large distortion when embedding real-world graphs with scale-free or hierarchical structure. We thank Professor Jure Leskovec for a great quarter in Fall 2019. The default parameters of the model are selected according to the best results obtained in the paper, and should provide a good performance on many node-level and graph-level tasks, without modifications. Design Space for Graph Neural Networks Jiaxuan You, Rex Ying, Jure Leskovec. ¡Using effective features over graphs is the key to achieving good test performance. In this paper, we develop a new strategy and self-supervised methods for pre-training Graph Neural Networks (GNNs). 2/14/21 Jure Leskovec, Stanford CS224W: Machine Learning with Graphs, 2 Prediction head Predictions Labels Loss function Evaluation metrics Graph Neural Network Node embeddings Input Graph (5) How do we split our dataset into train / validation / test set? 1. Mode: single, disjoint, mixed, batch. Hyperbolic Graph Convolutional Neural Networks Ines Chamiz Rex Yingy Christopher Re´ yJure Leskovec yDepartment of Computer Science, Stanford University zInstitute for Computational and Mathematical Engineering, Stanford University fchami, rexying, chrismre, jureg@cs.stanford.edu Abstract Graph convolutional neural networks (GCNs) embed nodes in a graph into Eu- Optimize embeddings according to: Given Ines Chami, Zhitao Ying, Christopher Ré, Jure Leskovec. ¡In this lecture, we overview the traditional features for: §Node-level prediction §Link-level prediction It was an inspiring experience to learn methods for analyzing graphs and explore the frontier of neural methods for graph. Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. CS224W is definitely a great course on networks, find the most up to date course website [here]. The key to the success of our strategy is to pre-train an expressive GNN at the level of individual nodes as well as entire graphs so that the GNN can learn useful local and global representations simultaneously. 12/3/19 Jure Leskovec, Stanford CS224W: Machine Learning with Graphs, http://cs224w.stanford.edu 2 … Output:Node embeddings. The defaults are as follows: ¡Traditional ML pipeline uses hand-designed features. ‪Professor of Computer Science, Stanford University‬ - ‪‪Cited by 79,326‬‬ - ‪Data mining‬ - ‪Machine Learning‬ - ‪Graph Neural Networks‬ - ‪Knowledge Graphs‬ - ‪Complex Networks‬ Hyperbolic Graph Convolutional Neural Networks Ines Chami∗‡ Rex Ying †Christopher Ré Jure Leskovec† †Department of Computer Science, Stanford University ‡Institute for Computational and Mathematical Engineering, Stanford University {chami, rexying, chrismre, jure}@cs.stanford.edu October 11, 2019 Abstract Graph convolutional neural networks (GCNs) map nodes in a graph …

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