67 papers with code ·
Graphs

No evaluation results yet. Help compare methods by
submit
evaluation metrics.

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).

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.

#2 best model for Graph Classification on RE-M5K

CVPR 2018 • fidler-lab/polyrnn-pp •

Manually labeling datasets with object masks is extremely time consuming.

pfnet-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.

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.

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.

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

xiangwang1223/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

We propose a new CogQA framework for multi-hop question answering in web-scale documents.

GRAPH NEURAL NETWORK MULTI-HOP READING COMPREHENSION QUESTION ANSWERING

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.