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Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
We propose a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. Expand
Representation Learning on Graphs: Methods and Applications
TLDR
We provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph neural networks. Expand
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
We develop a data-efficient Graph Convolutional Network (GCN) algorithm, which combines efficient random walks and graph convolutions to generate embeddings of nodes (i.e., items) that incorporate both graph structure as well as node feature information. Expand
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
TLDR
GraphRNN learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations, conditioned on the graph structure generated. Expand
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
TLDR
We propose Graph Convolutional Policy Network (GCPN), a general graph convolutional network based model for goal-directed graph generation through reinforcement learning. Expand
GNNExplainer: Generating Explanations for Graph Neural Networks
TLDR
We propose GnnExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Expand
Hyperbolic Graph Convolutional Neural Networks
TLDR
We propose Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs and hyper bolic geometry to learn inductive node representations for hierarchical and scale-free graphs. Expand
Position-aware Graph Neural Networks
TLDR
We propose Position-aware Graph Neural Networks (P-GNNs), a new class of GNNs for computing position-aware node embeddings. Expand
GraphRNN: A Deep Generative Model for Graphs
TLDR
We propose GraphRNN, a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure. Expand
Learning to Simulate Complex Physics with Graph Networks
TLDR
We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another. Expand
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