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Node Classification

120 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.

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

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Latest papers with code

Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised Classification

22 Jan 2020anonymous-sourcecode/DifNN

Existing graph neural networks may suffer from the "suspended animation problem" when the model architecture goes deep.

GRAPH REPRESENTATION LEARNING NODE CLASSIFICATION

0
22 Jan 2020

Graph-Bert: Only Attention is Needed for Learning Graph Representations

15 Jan 2020jwzhanggy/Graph-Bert

We have tested the effectiveness of GRAPH-BERT on several graph benchmark datasets.

GRAPH CLUSTERING NODE CLASSIFICATION

40
15 Jan 2020

DropEdge: Towards Deep Graph Convolutional Networks on Node Classification

ICLR 2020 DropEdge/DropEdge

Over-fitting and over-smoothing are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification.

NODE CLASSIFICATION

41
01 Jan 2020

Composition-based Multi-Relational Graph Convolutional Networks

ICLR 2020 malllabiisc/CompGCN

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

GRAPH CLASSIFICATION KNOWLEDGE GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

24
01 Jan 2020

A Non-negative Symmetric Encoder-Decoder Approach for Community Detection

CIKM 2019 benedekrozemberczki/karateclub

Latent factor models for community detection aim to find a distributed and generally low-dimensional representation, or coding, that captures the structural regularity of network and reflects the community membership of nodes.

COMMUNITY DETECTION GRAPH CLUSTERING NETWORK EMBEDDING NODE CLASSIFICATION

312
24 Dec 2019

Gaussian Embedding of Large-scale Attributed Graphs

2 Dec 2019bhagya-hettige/GLACE

Graph embedding methods transform high-dimensional and complex graph contents into low-dimensional representations.

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION

0
02 Dec 2019

ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations

18 Nov 2019malllabiisc/ASAP

Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification.

GRAPH CLASSIFICATION LINK PREDICTION NODE CLASSIFICATION

11
18 Nov 2019

On the choice of graph neural network architectures

13 Nov 2019cvignac/gnn_statistics

Seminal works on graph neural networks have primarily targeted semi-supervised node classification problems with few observed labels and high-dimensional signals.

NODE CLASSIFICATION

0
13 Nov 2019

A Capsule Network-based Model for Learning Node Embeddings

12 Nov 2019daiquocnguyen/Caps2NE

Existing node embedding models often suffer from a limitation of exploiting graph information to infer plausible embeddings of unseen nodes.

NODE CLASSIFICATION

4
12 Nov 2019