Node Classification
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.
State-of-the-art leaderboards
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Latest papers without code
Deep Graph Infomax
We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.
MILE: A Multi-Level Framework for Scalable Graph Embedding
We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs.
Adversarial Attacks on Graph Neural Networks via Meta Learning
Deep learning models for graphs have advanced the state of the art on many tasks.
CGNF: Conditional Graph Neural Fields
By integrating the conditional random fields (CRF) in the graph convolutional networks, we explicitly model a joint probability of the entire set of node labels, thus taking advantage of neighborhood label information in the node label prediction task.
Few-shot Classification on Graphs with Structural Regularized GCNs
We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning.
Compositional Network Embedding
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Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.
Semi-Supervised Graph Classification: A Hierarchical Graph Perspective
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We study the node classification problem in the hierarchical graph where a `node' is a graph instance, e. g., a user group in the above example.
Topological based classification of paper domains using graph convolutional networks
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The main approaches for node classification in graphs are information propagation and the association of the class of the node with external information.
Node Embedding over Temporal Graphs
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In this work, we present a method for node embedding in temporal graphs.
A Comprehensive Comparison of Unsupervised Network Representation Learning Methods
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However, there is no common ground for systematic comparison of embeddings to understand their behavior for different graphs and tasks.