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Attributed network embedding aims to learn low-dimensional vector representations for nodes in a network, where each node contains rich attributes/features describing node content.
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
Over the last few years, graph autoencoders (AE) and variational autoencoders (VAE) emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
Graph autoencoders (AE) and variational autoencoders (VAE) recently emerged as powerful node embedding methods, with promising performances on challenging tasks such as link prediction and node clustering.
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.
SOTA for Node Clustering on IMDb
The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.
#7 best model for Heterogeneous Node Classification on DBLP (PACT) 14k
In this paper, we propose a unified framework for attributed network embedding-attri2vec-that learns node embeddings by discovering a latent node attribute subspace via a network structure guided transformation performed on the original attribute space.
SOTA for Node Clustering on Facebook
In this study we show that in-order to extract high-quality context-sensitive node representations it is not needed to rely on supplementary node features, nor to employ computationally heavy and complex models.