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Node Classification

86 papers with code · Graphs

The node classification task is one where the algorithm has to determine the labelling of samples (represented as nodes) by looking at the labels of their neighbours.

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Latest papers with code

GEMSEC: Graph Embedding with Self Clustering

ASONAM 2019 benedekrozemberczki/GEMSEC

In this paper we propose GEMSEC - a graph embedding algorithm which learns a clustering of the nodes simultaneously with the embedding.

COMMUNITY DETECTION GRAPH EMBEDDING NETWORK EMBEDDING NODE CLASSIFICATION

97
27 Aug 2019

EdMot: An Edge Enhancement Approach for Motif-aware Community Detection

KDD 2019 benedekrozemberczki/EdMot

Based on the new edge set, the original connectivity structure of the input network is enhanced to generate a rewired network, whereby the motif-based higher-order structure is leveraged and the hypergraph fragmentation issue is well addressed.

COMMUNITY DETECTION NODE CLASSIFICATION

40
04 Aug 2019

GraphSAINT: Graph Sampling Based Inductive Learning Method

10 Jul 2019GraphSAINT/GraphSAINT

Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training.

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

3
10 Jul 2019

Signed Graph Attention Networks

26 Jun 2019huangjunjie95/SiGAT

Recently, Graph Neural Network (GNN) is proposed as a general and powerful framework to handle tasks on graph data, e. g., node embedding, link prediction and node classification.

GRAPH NEURAL NETWORK LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

3
26 Jun 2019

Graph Star Net for Generalized Multi-Task Learning

21 Jun 2019graph-star-team/graph_star

In this work, we present graph star net (GraphStar), a novel and unified graph neural net architecture which utilizes message-passing relay and attention mechanism for multiple prediction tasks - node classification, graph classification and link prediction.

GRAPH CLASSIFICATION LINK PREDICTION MULTI-TASK LEARNING NODE CLASSIFICATION SENTIMENT ANALYSIS TEXT CLASSIFICATION

31
21 Jun 2019

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

12 Jun 2019xiangyue9607/BioNEV

Motivation: Graph embedding learning which aims to automatically learn low-dimensional node representations has drawn increasing attention in recent years.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

24
12 Jun 2019

Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks

5 Jun 2019y-fujiwr/Stronger_GCN

Recently, neural network based approaches have achieved significant improvement for solving large, complex, graph-structured problems.

NODE CLASSIFICATION

1
05 Jun 2019

Triple2Vec: Learning Triple Embeddings from Knowledge Graphs

28 May 2019Chrisackerman1/Triple2Vec-Learning-Triple-Embeddings-from-Knowledge-Graphs

We show that directly applying existing embedding techniques on the nodes of the line graph to learn edge embeddings is not enough in the context of knowledge graphs.

GRAPH EMBEDDING KNOWLEDGE GRAPHS NODE CLASSIFICATION

0
28 May 2019

Graph Attention Auto-Encoders

26 May 2019amin-salehi/GATE

Moreover, node representations are regularized to reconstruct the graph structure.

NODE CLASSIFICATION UNSUPERVISED REPRESENTATION LEARNING

1
26 May 2019

Meta-GNN: On Few-shot Node Classification in Graph Meta-learning

23 May 2019AI-DL-Conference/Meta-GNN

Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i. e., acquiring new knowledge and skills with little or even no demonstration.

FEW-SHOT LEARNING META-LEARNING NODE CLASSIFICATION

8
23 May 2019