It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.

MULTI-CLASS CLASSIFICATION NETWORK EMBEDDING NODE CLUSTERING

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types.

Ranked #1 on Node Clustering on IMDb

GRAPH EMBEDDING LINK PREDICTION NODE CLASSIFICATION NODE CLUSTERING

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.

Learning high-level representations for graphs is of great importance for graph analysis tasks.

The derived node representations can be used to serve various downstream tasks, such as node classification and node clustering.

Ranked #7 on Heterogeneous Node Classification on DBLP (PACT) 14k

CLASSIFICATION GRAPH REPRESENTATION LEARNING HETEROGENEOUS NODE CLASSIFICATION NODE CLUSTERING

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

Ranked #2 on Graph Clustering on Cora

Experimental results show that AGE consistently outperforms state-of-the-art graph embedding methods considerably on these tasks.

Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts.

Ranked #1 on Node Clustering on Wiki

Graph neural networks have been widely used on modeling graph data, achieving impressive results on node classification and link prediction tasks.

Ranked #1 on Graph Classification on Tox21

GRAPH CLASSIFICATION GRAPH CLUSTERING GRAPH EMBEDDING GRAPH GENERATION GRAPH LEARNING GRAPH RECONSTRUCTION NODE CLUSTERING