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Hierarchical Graph Representation Learning with Differentiable Pooling
TLDR
DiffPool is proposed, 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.
Representation Learning on Graphs: Methods and Applications
TLDR
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 are provided.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
TLDR
A novel method based on highly efficient random walks to structure the convolutions and a novel training strategy that relies on harder-and-harder training examples to improve robustness and convergence of the model are developed.
GNNExplainer: Generating Explanations for Graph Neural Networks
TLDR
GnExplainer is proposed, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task.
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models
TLDR
The experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation
TLDR
Graph Convolutional Policy Network (GCPN) is proposed, a general graph convolutional network based model for goal-directed graph generation through reinforcement learning that can achieve 61% improvement on chemical property optimization over state-of-the-art baselines while resembling known molecules, and achieve 184% improved on the constrained property optimization task.
Hyperbolic Graph Convolutional Neural Networks
TLDR
This work proposes Hyperbolic Graph Convolutional Neural Network (HGCN), the first inductive hyperbolic GCN that leverages both the expressiveness of GCNs andhyperbolic geometry to learn inductive node representations for hierarchical and scale-free graphs.
Position-aware Graph Neural Networks
TLDR
Position-aware Graph Neural Networks (P-GNNs) are proposed, a new class of GNNs for computing position-aware node embeddings that are inductive, scalable, and can incorporate node feature information.
Learning to Simulate Complex Physics with Graph Networks
TLDR
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, and holds promise for solving a wide range of complex forward and inverse problems.
GraphRNN: A Deep Generative Model for Graphs
TLDR
The experiments show that GraphRNN significantly outperforms all baselines, learning to generate diverse graphs that match the structural characteristics of a target set, while also scaling to graphs 50 times larger than previous deep models.
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