ComplEx model trained with a nuclear norm regularizer; A relation prediction objective is added on top of the commonly used 1vsAll objective.

learn to explore the sample relationships via transformer networks

BatchFormer

BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

ComplEx-N3-RP

Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations

A **Graph Attention Network (GAT)** is a neural network architecture that operates on graph-structured data, leveraging masked self-attentional layers to address the shortcomings of prior methods based on graph convolutions or their approximations. By stacking layers in which nodes are able to attend over their neighborhoods’ features, a GAT enables (implicitly) specifying different weights to different nodes in a neighborhood, without requiring any kind of costly matrix operation (such as inversion) or depending on knowing the graph structure upfront.

See [here](https://docs.dgl.ai/en/0.4.x/tutorials/models/1_gnn/9_gat.html) for an explanation by DGL.

Source	Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com