48 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.

We present Deep Graph Infomax (DGI), a general approach for learning node representations within graph-structured data in an unsupervised manner.

We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs.

Deep learning models for graphs have advanced the state of the art on many tasks.

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.

We consider the fundamental problem of semi-supervised node classification in attributed graphs with a focus on \emph{few-shot} learning.

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Node ID is not generalizable and, thus, the existing methods have to pay great effort in cold-start problem.

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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.

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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.

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In this work, we present a method for node embedding in temporal graphs.

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However, there is no common ground for systematic comparison of embeddings to understand their behavior for different graphs and tasks.