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Graph Neural Network

67 papers with code · Graphs

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Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs

CVPR 2018 JudyYe/zero-shot-gcn

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

GRAPH NEURAL NETWORK KNOWLEDGE GRAPHS ZERO-SHOT LEARNING

Capsule Graph Neural Network

ICLR 2019 benedekrozemberczki/CapsGNN

The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK

Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks in Molecular Graph Analysis

4 Feb 2019pfnet-research/chainer-chemistry

Graph Neural Network (GNN) is a popular architecture for the analysis of chemical molecules, and it has numerous applications in material and medicinal science.

GRAPH NEURAL NETWORK

Few-Shot Learning with Graph Neural Networks

ICLR 2018 vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

ACTIVE LEARNING FEW-SHOT LEARNING GRAPH NEURAL NETWORK

Few-Shot Learning with Graph Neural Networks

10 Nov 2017vgsatorras/few-shot-gnn

We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not.

ACTIVE LEARNING FEW-SHOT LEARNING GRAPH NEURAL NETWORK

Hierarchical Graph Representation Learning with Differentiable Pooling

NeurIPS 2018 RexYing/diffpool

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

KGAT: Knowledge Graph Attention Network for Recommendation

20 May 2019xiangwang1223/knowledge_graph_attention_network

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.

GRAPH NEURAL NETWORK KNOWLEDGE GRAPHS RECOMMENDATION SYSTEMS

Graph Edit Distance Computation via Graph Neural Networks

16 Aug 2018benedekrozemberczki/SimGNN

Graph similarity/distance computation, such as Graph Edit Distance (GED) and Maximum Common Subgraph (MCS), is the core operation of graph similarity search and many other applications, but very costly to compute in practice.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK GRAPH SIMILARITY