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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
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
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
GNN Explainer: A Tool for Post-hoc Explanation of Graph Neural Networks
TLDR
We propose GnnExplainer, a general model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task (node and graph classification, link prediction). Expand
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
TLDR
We propose Hierarchical Temporal Convolutional Networks (HierTCN), a hierarchical deep learning architecture that makes dynamic recommendations based on users' sequential multi-session interactions with items. Expand
Design Space for Graph Neural Networks
TLDR
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing number of new architectures as well as novel applications. Expand
Redundancy-Free Computation for Graph Neural Networks
TLDR
We propose Hierarchically Aggregated computation Graphs, a GNN representation technique that explicitly avoids redundancy by managing intermediate aggregation results hierarchically and eliminates repeated computations and unnecessary data transfers in GNN training and inference. Expand
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