23 papers with code ·
Graphs

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations.

SOTA for Text Classification on R8

GRAPH CLASSIFICATION IMAGE CLASSIFICATION RELATION EXTRACTION SENTIMENT ANALYSIS TEXT CLASSIFICATION

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.

SOTA for Graph Classification on PROTEINS

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 REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

Recent works on representation learning for graph structured data predominantly focus on learning distributed representations of graph substructures such as nodes and subgraphs.

ICLR 2019 • weihua916/powerful-gnns •

Here, we present a theoretical framework for analyzing the expressive power of GNNs to capture different graph structures.

CVPR 2017 • mys007/ecc •

A number of problems can be formulated as prediction on graph-structured data.

The problem of accurately measuring the similarity between graphs is at the core of many applications in a variety of disciplines.

KDD 2018 • benedekrozemberczki/GAM •

Graph classification is a problem with practical applications in many different domains.

giannisnik/cnn-graph-classification •

•Graph kernels have been successfully applied to many graph classification problems.

ICLR 2018 • xbresson/spatial_graph_convnets •

In this paper, we are interested to design neural networks for graphs with variable length in order to solve learning problems such as vertex classification, graph classification, graph regression, and graph generative tasks.