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Found 12 papers, 9 papers with code
Recipe for a General, Powerful, Scalable Graph Transformer
We propose a recipe on how to build a general, powerful, scalable (GPS) graph Transformer with linear complexity and state-of-the-art results on a diverse set of benchmarks.
Ranked #1 on
Graph Regression
on ZINC-500k
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning.
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics.
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
An emerging trend in representation learning over knowledge graphs (KGs) moves beyond transductive link prediction tasks over a fixed set of known entities in favor of inductive tasks that imply training on one graph and performing inference over a new graph with unseen entities.
Ranked #1 on
Inductive Link Prediction
on ILPC22-Small
Systematic Clinical Evaluation of A Deep Learning Method for Medical Image Segmentation: Radiosurgery Application
We show that the segmentation model reduces the ratio of detection disagreements from 0. 162 to 0. 085 (p < 0. 05).
Improving Inductive Link Prediction Using Hyper-Relational Facts
In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks.
NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphs
To this end, we propose NodePiece, an anchor-based approach to learn a fixed-size entity vocabulary.
Ranked #8 on
Link Property Prediction
on ogbl-wikikg2
Query Embedding on Hyper-relational Knowledge Graphs
Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.
Message Passing for Hyper-Relational Knowledge Graphs
We also demonstrate that existing benchmarks for evaluating link prediction (LP) performance on hyper-relational KGs suffer from fundamental flaws and thus develop a new Wikidata-based dataset - WD50K.
Ranked #1 on
Link Prediction
on WD50K
El Departamento de Nosotros: How Machine Translated Corpora Affects Language Models in MRC Tasks
Pre-training large-scale language models (LMs) requires huge amounts of text corpora.
Bringing Light Into the Dark: A Large-scale Evaluation of Knowledge Graph Embedding Models Under a Unified Framework
The heterogeneity in recently published knowledge graph embedding models' implementations, training, and evaluation has made fair and thorough comparisons difficult.
Deep Learning for Brain Tumor Segmentation in Radiosurgery: Prospective Clinical Evaluation
Stereotactic radiosurgery is a minimally-invasive treatment option for a large number of patients with intracranial tumors.
