Graph Representation Learning

2 benchmarks982 papers

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

<span class="description-source">Source: SIGN: Scalable Inception Graph Neural Networks </span>

Benchmarks

Graph Representation Learning on COMA

Graph Representation Learning on FB15k