32 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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Complex networks are used as an abstraction for systems modeling in physics, biology, sociology, and other areas.

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Network representation learning (NRL) has been widely used to help analyze large-scale networks through mapping original networks into a low-dimensional vector space.

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To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention.

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We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network.

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Recent graph neural networks implement convolutional layers based on polynomial filters operating in the spectral domain.

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Learning latent representations of nodes in graphs is an important and ubiquitous task with widespread applications such as link prediction, node classification, and graph visualization.

GRAPH EMBEDDING GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLASSIFICATION

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Graph convolutional network (GCN) is an emerging neural network approach.

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Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data.

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In this paper, we study the problem of node embedding in multi-layered graphs and propose a deep method that embeds nodes using both relations (connections within and between layers of the graph) and nodes signals.

GRAPH EMBEDDING LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION

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We perform a thorough empirical evaluation of four prominent GNN models and show that considering different splits of the data leads to dramatically different rankings of models.