10 papers with code ·

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.

COMMUNITY DETECTION LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION NODE CLUSTERING

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

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

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

GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION NODE CLUSTERING

Node embeddings have become an ubiquitous technique for representing graph data in a low dimensional space.

#2 best model for Graph Clustering on Cora

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

LINK PREDICTION NETWORK EMBEDDING NODE CLASSIFICATION NODE CLUSTERING

Network representation learning (NRL) is a powerful technique for learning low-dimensional vector representation of high-dimensional and sparse graphs.

COMMUNITY DETECTION LINK PREDICTION NODE CLUSTERING REPRESENTATION LEARNING

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.

COMMUNITY DETECTION GRAPH REPRESENTATION LEARNING LINK PREDICTION NODE CLUSTERING