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Graph Classification

23 papers with code · Graphs

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How Powerful are Graph Neural Networks?

ICLR 2019 Keyulu Xu* et al

Graph Neural Networks (GNNs) for representation learning of graphs broadly follow a neighborhood aggregation framework, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes.

GRAPH CLASSIFICATION GRAPH REPRESENTATION LEARNING

01 May 2019

Graph Transformer

ICLR 2019 Yuan Li et al

Graph neural networks (GNN) have gained increasing research interests as a mean to the challenging goal of robust and universal graph learning.

FEW-SHOT LEARNING GRAPH CLASSIFICATION KNOWLEDGE GRAPHS

01 May 2019

DEEP GEOMETRICAL GRAPH CLASSIFICATION

ICLR 2019 Mostafa Rahmani et al

In the second step, the GNN is applied to the point-cloud representation of the graph provided by the embedding method.

GRAPH CLASSIFICATION GRAPH CLUSTERING GRAPH EMBEDDING

01 May 2019

Graph Classification with Geometric Scattering

ICLR 2019 Feng Gao et al

Furthermore, ConvNets inspired recent advances in geometric deep learning, which aim to generalize these networks to graph data by applying notions from graph signal processing to learn deep graph filter cascades.

GRAPH CLASSIFICATION IMAGE CLASSIFICATION

01 May 2019

Supervised Community Detection with Line Graph Neural Networks

ICLR 2019 Zhengdao Chen et al

We study data-driven methods for community detection on graphs, an inverse problem that is typically solved using the spectrum of certain operators or via posterior inference under certain probabilistic graphical models.

COMMUNITY DETECTION GRAPH CLASSIFICATION

01 May 2019

DDGK: Learning Graph Representations for Deep Divergence Graph Kernels

21 Apr 2019Rami Al-Rfou et al

Second, for each pair of graphs, we train a cross-graph attention network which uses the node representations of an anchor graph to reconstruct another graph.

GRAPH CLASSIFICATION GRAPH SIMILARITY

21 Apr 2019

A General Neural Network Architecture for Persistence Diagrams and Graph Classification

20 Apr 2019Mathieu Carrière et al

In this article, we propose to use extended persistence diagrams to efficiently encode graph structure.

GRAPH CLASSIFICATION

20 Apr 2019

Self-Attention Graph Pooling

17 Apr 2019Junhyun Lee et al

In particular, studies have focused on generalizing convolutional neural networks to graph data, which includes redefining the convolution and the downsampling (pooling) operations for graphs.

GRAPH CLASSIFICATION

17 Apr 2019

Semi-Supervised Graph Classification: A Hierarchical Graph Perspective

10 Apr 2019Jia Li et al

We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.

GRAPH CLASSIFICATION GRAPH EMBEDDING NODE CLASSIFICATION

10 Apr 2019

Learning Aligned-Spatial Graph Convolutional Networks for Graph Classification

6 Apr 2019Lu Bail et al

In this paper, we develop a novel Aligned-Spatial Graph Convolutional Network (ASGCN) model to learn effective features for graph classification.

GRAPH CLASSIFICATION

06 Apr 2019