Graph Convolutional Network

260 papers with code • 0 benchmarks • 0 datasets

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Use these libraries to find Graph Convolutional Network models and implementations

Most implemented papers

Variational Graph Auto-Encoders

tkipf/gae 21 Nov 2016

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE).

Deep Graph Infomax

PetarV-/DGI ICLR 2019

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.

Graph Convolutional Networks for Text Classification

yao8839836/text_gcn 15 Sep 2018

We build a single text graph for a corpus based on word co-occurrence and document word relations, then learn a Text Graph Convolutional Network (Text GCN) for the corpus.

T-GCN: A Temporal Graph ConvolutionalNetwork for Traffic Prediction

lehaifeng/T-GCN 12 Nov 2018

However, traffic forecasting has always been considered an open scientific issue, owing to the constraints of urban road network topological structure and the law of dynamic change with time, namely, spatial dependence and temporal dependence.

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

IBM/EvolveGCN 26 Feb 2019

Existing approaches typically resort to node embeddings and use a recurrent neural network (RNN, broadly speaking) to regulate the embeddings and learn the temporal dynamics.

Graph Neural Networks: A Review of Methods and Applications

thunlp/GNNPapers 20 Dec 2018

Lots of learning tasks require dealing with graph data which contains rich relation information among elements.

Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks

google-research/google-research KDD 2019

Furthermore, Cluster-GCN allows us to train much deeper GCN without much time and memory overhead, which leads to improved prediction accuracy---using a 5-layer Cluster-GCN, we achieve state-of-the-art test F1 score 99. 36 on the PPI dataset, while the previous best result was 98. 71 by [16].

G-TAD: Sub-Graph Localization for Temporal Action Detection

Frostinassiky/gtad CVPR 2020

In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem.

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

JudyYe/zero-shot-gcn CVPR 2018

Given a learned knowledge graph (KG), our approach takes as input semantic embeddings for each node (representing visual category).

Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition

benedekrozemberczki/pytorch_geometric_temporal CVPR 2019

In addition, the second-order information (the lengths and directions of bones) of the skeleton data, which is naturally more informative and discriminative for action recognition, is rarely investigated in existing methods.