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Graph Classification

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Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning

19 Aug 2019

Assignment kernels are based on an optimal bijection between the parts and have proven to be an effective alternative to the established convolution kernels.

GRAPH CLASSIFICATION

Graph Neural Networks for Small Graph and Giant Network Representation Learning: An Overview

1 Aug 2019

Several different types of graph neural network models have been introduced for learning the representations from such different types of graphs already.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK REPRESENTATION LEARNING

InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

31 Jul 2019

There are also some recent methods based on language models (e. g. graph2vec) but they tend to only consider certain substructures (e. g. subtrees) as graph representatives.

GRAPH CLASSIFICATION UNSUPERVISED REPRESENTATION LEARNING

Topology Based Scalable Graph Kernels

15 Jul 2019

We propose a new graph kernel for graph classification and comparison using Ollivier Ricci curvature.

GRAPH CLASSIFICATION

k-hop Graph Neural Networks

13 Jul 2019

We show that the proposed architecture can identify fundamental graph properties.

GRAPH CLASSIFICATION NODE CLASSIFICATION

Semi-Supervised Graph Embedding for Multi-Label Graph Node Classification

12 Jul 2019

The dimension of the label vector is the same as that of the node vector before the last convolution operation of GCN.

GRAPH CLASSIFICATION GRAPH EMBEDDING MULTI-LABEL CLASSIFICATION MULTI-LABEL LEARNING NODE CLASSIFICATION

Label Aware Graph Convolutional Network -- Not All Edges Deserve Your Attention

10 Jul 2019

To solve this problem, one usually calculates a low-dimensional representation for each node in the graph with supervised or unsupervised approaches.

GRAPH CLASSIFICATION NODE CLASSIFICATION REPRESENTATION LEARNING

Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation

4 Jul 2019

Graph Neural Networks (GNNs) are powerful to learn the representation of graph-structured data.

GRAPH CLASSIFICATION

iPool -- Information-based Pooling in Hierarchical Graph Neural Networks

1 Jul 2019

In this paper, we propose a parameter-free pooling operator, called iPool, that permits to retain the most informative features in arbitrary graphs.

GRAPH CLASSIFICATION

Mincut pooling in Graph Neural Networks

30 Jun 2019

The advance of node pooling operations in Graph Neural Networks (GNNs) has lagged behind the feverish design of new message-passing techniques, and pooling remains an important and challenging endeavor for the design of deep architectures.

GRAPH CLASSIFICATION GRAPH NEURAL NETWORK SEMANTIC SEGMENTATION