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Graph Classification

380 papers with code • 65 benchmarks • 46 datasets

Graph Classification is a task that involves classifying a graph-structured data into different classes or categories. Graphs are a powerful way to represent relationships and interactions between different entities, and graph classification can be applied to a wide range of applications, such as social network analysis, bioinformatics, and recommendation systems. In graph classification, the input is a graph, and the goal is to learn a classifier that can accurately predict the class of the graph.

( Image credit: Hierarchical Graph Pooling with Structure Learning )

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GNNExplainer: Generating Explanations for Graph Neural Networks

RexYing/gnn-model-explainer • • NeurIPS 2019

We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures.

10
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Strategies for Pre-training Graph Neural Networks

snap-stanford/pretrain-gnns • • ICLR 2020

Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training.

10
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Principal Neighbourhood Aggregation for Graph Nets

lukecavabarrett/pna • • NeurIPS 2020

Graph Neural Networks (GNNs) have been shown to be effective models for different predictive tasks on graph-structured data.

8
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Structural Deep Network Embedding

shenweichen/GraphEmbedding • • KDD 2016

Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem.

6
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graph2vec: Learning Distributed Representations of Graphs

benedekrozemberczki/karateclub • 17 Jul 2017

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

6
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SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

rusty1s/pytorch_geometric • • CVPR 2018

We present Spline-based Convolutional Neural Networks (SplineCNNs), a variant of deep neural networks for irregular structured and geometric input, e. g., graphs or meshes.

5
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InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization

fanyun-sun/InfoGraph • • ICLR 2020

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.

5
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Persistence Images: A Stable Vector Representation of Persistent Homology

CSU-TDA/PersistenceImages • 22 Jul 2015

We convert a PD to a finite-dimensional vector representation which we call a persistence image (PI), and prove the stability of this transformation with respect to small perturbations in the inputs.

4
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Geometric deep learning on graphs and manifolds using mixture model CNNs

dmlc/dgl • • CVPR 2017

Recently, there has been an increasing interest in geometric deep learning, attempting to generalize deep learning methods to non-Euclidean structured data such as graphs and manifolds, with a variety of applications from the domains of network analysis, computational social science, or computer graphics.

4
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Supervised Community Detection with Line Graph Neural Networks

zhengdao-chen/GNN4CD • • ICLR 2019

We show that, in a data-driven manner and without access to the underlying generative models, they can match or even surpass the performance of the belief propagation algorithm on binary and multi-class stochastic block models, which is believed to reach the computational threshold.

4
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