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Found 9 papers, 7 papers with code
Enhancing ID and Text Fusion via Alternative Training in Session-based Recommendation
To integrate text information, various methods have been introduced, mostly following a naive fusion framework.
Mixture of Link Predictors
Link prediction, which aims to forecast unseen connections in graphs, is a fundamental task in graph machine learning.
Distance-Based Propagation for Efficient Knowledge Graph Reasoning
A new class of methods have been proposed to tackle this problem by aggregating path information.
LPFormer: An Adaptive Graph Transformer for Link Prediction
In recent years, a new class of methods has emerged that combines the advantages of message-passing neural networks (MPNN) and heuristics methods.
Revisiting Link Prediction: A Data Perspective
We recognize three fundamental factors critical to link prediction: local structural proximity, global structural proximity, and feature proximity.
Evaluating Graph Neural Networks for Link Prediction: Current Pitfalls and New Benchmarking
Furthermore, new and diverse datasets have also been created to better evaluate the effectiveness of these new models.
Learning Representations for Hyper-Relational Knowledge Graphs
It motivates us to design a framework that utilizes multiple aggregators to learn representations for hyper-relational facts: one from the perspective of the base triple and the other one from the perspective of the qualifiers.
Are Message Passing Neural Networks Really Helpful for Knowledge Graph Completion?
This suggests a conflation of scoring function design, loss function design, and MP in prior work, with promising insights regarding the scalability of state-of-the-art KGC methods today, as well as careful attention to more suitable MP designs for KGC tasks tomorrow.
Graph Enhanced BERT for Query Understanding
In other words, GE-BERT can capture both the semantic information and the users' search behavioral information of queries.
