Knowledge Enhanced Graph Neural Networks for Graph Completion

Understand

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web.

• However, while being rich in information, graphs are often noisy and incomplete.
• As a result, graph completion tasks, such as node classification or link prediction, have gained attention.
• On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs.