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Semi-supervised node classification via GCN, Deep Graph Infomax and fine-tuning¶. This demo demonstrates how to perform semi-supervised node classification, using the Deep Graph Infomax algorithm and GCN on the Cora dataset. It uses very few labelled training examples, demonstrating the benefits of pre-training a model with Deep Graph Infomax for …

Semi-supervised learning. Graph node classification. 1. Introduction. Recent approaches in semi-supervised graph node classification via graph neural networks (GNNs) focus on devising different neural architectures so that unlabeled nodes could affect the predictions on labeled nodes by intermediate aggregations.

NodeAug: Semi-Supervised Node Classification with Data Augmentation Yiwei Wang National University of Singapore Singapore wangyw_seu@foxmail.com Wei Wang National University of Singapore Singapore wangwei@comp.nus.edu.sg Yuxuan Liang National University of Singapore Singapore yuxliang@outlook.com Yujun Cai Nanyang Technological University Singapore

Semi-Supervised Node Classification. FGANN Architecture Having introduced a flexible graph attentional layer for efficient information propagation on graphs, we then construct FGANN model f A, X for the problem of semi-supervised node classification. In the following part, ...

Recent approaches in semi-supervised graph node classification via graph neural networks (GNNs) focus on devising different neural architectures so that ...

Dec 24, 2020 · Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user's private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more efficient than graph neural networks.

In this work, we encode the graph structure directly using a neural network model f(X, A) and train on a supervised target L0 for all nodes with labels, thereby ...

Semi-Supervised node classification is a fundamental task on graph data, which aims to classify the nodes in an (attributed) graph given the ...

Collective Classification Models

For instance, for semi-supervised node classification in citation networks, where nodes denote articles and edges represent citation, the task is to predict the ...

18.05.2021 · Semi-supervised node classification on graph-structured data has many applications such as fraud detection, fake account and review detection, user’s private attribute inference in social networks, and community detection. Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised …

By using Data Augmentation (DA), we present a new method to enhance Graph Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised node classification. DA for graph data remains under-explored. Due to the connections built by edges, DA for different nodes influence each other and lead to undesired results, such as uncontrollable DA magnitudes and changes of ground-truth labels.

Topology-Imbalance Learning for Semi-Supervised Node Classification Introduction. Code for NeurIPS 2021 paper "Topology-Imbalance Learning for Semi-Supervised Node Classification"This work investigates the topology-imbalance problem of node representation learning on graph-structured data. Unlike the "quantity-imbalance" problem, the topology …

Semi-supervised node classification via GCN, Deep Graph Infomax and fine-tuning¶ · Pre-train a GCN model using Deep Graph Infomax, without any labelled data.

Title:Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information ... Abstract: The nodes of a graph ...

Various methods such as pairwise Markov Random Fields (pMRF) and graph neural networks were developed for semi-supervised node classification. pMRF is more ...

06.12.2021 · Semi-supervised object classification has been a fundamental problem in relational data modeling recently. The problem has been extensively studied in the literature of graph neural networks (GNNs). Based on the homophily assumption, GNNs smooth the features of the adjacent nodes, resulting in hybrid class distributions in the feature space ...

Specifically, node embedding methods provide continuous representations for vertices that has proved to be quite useful for prediction tasks, and Graph Neural Networks (GNNs) have recently been used for semi-supervised node and graph classification tasks with great success.

ABSTRACT. By using Data Augmentation (DA), we present a new method to enhance Graph Convolutional Networks (GCNs), that are the state-of-the-art models for semi-supervised node classification. DA for graph data remains under-explored. Due to the connections built by edges, DA for different nodes influence each other and lead to undesired results, such as uncontrollable DA magnitudes and changes of ground-truth labels.

Semi-Supervised node classificationis a fundamental task on graph data, which aims to classify the nodes in an (attributed) graph given the class labels of a few nodes [19]. For this task, Graph Convolutional Networks (GCNs) have achieved state-of-the- art performance [30].

Systematic experiments demonstrate the effectiveness and generalizability of our method in relieving topology-imbalance issue and promoting semi-supervised node classification. The further analysis unveils varied sensitivity of different graph neural networks (GNNs) to topology imbalance, which may serve as a new perspective in evaluating GNN architectures.

This tutorial will show how to build such a GNN for semi-supervised node classification with only a small number of labels on the Cora dataset, a citation ...

29.09.2020 · Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information 29 Sep 2020 · Mohammad Esmaeili , Aria Nosratinia · Edit social preview. The nodes of a graph existing in a cluster are more likely to ...