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Node Classification

789 papers with code • 122 benchmarks • 69 datasets

Node Classification is a machine learning task in graph-based data analysis, where the goal is to assign labels to nodes in a graph based on the properties of nodes and the relationships between them.

Node Classification models aim to predict non-existing node properties (known as the target property) based on other node properties. Typical models used for node classification consists of a large family of graph neural networks. Model performance can be measured using benchmark datasets like Cora, Citeseer, and Pubmed, among others, typically using Accuracy and F1.

( Image credit: Fast Graph Representation Learning With PyTorch Geometric )

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Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering

mdeff/cnn_graph • • NeurIPS 2016

In this work, we are interested in generalizing convolutional neural networks (CNNs) from low-dimensional regular grids, where image, video and speech are represented, to high-dimensional irregular domains, such as social networks, brain connectomes or words' embedding, represented by graphs.

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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.

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Representation Learning on Graphs with Jumping Knowledge Networks

dmlc/dgl • • ICML 2018

Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.

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Fast Graph Representation Learning with PyTorch Geometric

rusty1s/pytorch_geometric • • 6 Mar 2019

We introduce PyTorch Geometric, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch.

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Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

xiangyue9607/BioNEV • 12 Jun 2019

Our experimental results demonstrate that the recent graph embedding methods achieve promising results and deserve more attention in the future biomedical graph analysis.

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DeepGCNs: Making GCNs Go as Deep as CNNs

lightaime/deep_gcns_torch • • 15 Oct 2019

This work transfers concepts such as residual/dense connections and dilated convolutions from CNNs to GCNs in order to successfully train very deep GCNs.

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Composition-based Multi-Relational Graph Convolutional Networks

malllabiisc/CompGCN • • ICLR 2020

Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.

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Geom-GCN: Geometric Graph Convolutional Networks

graphdml-uiuc-jlu/geom-gcn • • ICLR 2020

From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses.

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Inductive Representation Learning on Temporal Graphs

StatsDLMathsRecomSys/Inductive-representation-learning-on-temporal-graphs • • ICLR 2020

Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

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ktrain: A Low-Code Library for Augmented Machine Learning

amaiya/ktrain • • 19 Apr 2020

We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply.

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