We propose Concept2Box, a novel approach that jointly embeds the two views of a KG using dual geometric representations.
However, the causal relationship between the two variables was largely ignored for learning to predict links on a graph.
Ranked #1 on Link Property Prediction on ogbl-ddi
Existing work linearize table cells and heavily rely on modifying deep language models such as BERT which only captures related cells information in the same table.
In this work, we present a novel framework called CoEvoGNN for modeling dynamic attributed graph sequence.
The user embeddings preserve spatial patterns and temporal patterns of a variety of periodicity (e. g., hourly, weekly, and weekday patterns).