229 citations · 553 across the 32 of their papers we have counts for
10 papers · 1 filter
Weisfeiler and Leman Go Relational
Pablo Barcelo, Mikhail Galkin, Christopher Morris +1
Knowledge graphs, modeling multi-relational data, improve numerous applications such as question answering or graph logical reasoning. Many graph neural networks for such data emer…
Inductive Logical Query Answering in Knowledge Graphs
Mikhail Galkin, Zhaocheng Zhu, Hongyu Ren +1
Formulating and answering logical queries is a standard communication interface for knowledge graphs (KGs). Alleviating the notorious incompleteness of real-world KGs, neural metho…
Using Graph Algorithms to Pretrain Graph Completion Transformers
Jonathan Pilault, Michael Galkin, Bahare Fatemi +3
Recent work on Graph Neural Networks has demonstrated that self-supervised pretraining can further enhance performance on downstream graph, link, and node classification tasks. How…
A Decade of Knowledge Graphs in Natural Language Processing: A Survey
Phillip Schneider, Tim Schopf, Juraj Vladika +3
In pace with developments in the research field of artificial intelligence, knowledge graphs (KGs) have attracted a surge of interest from both academia and industry. As a represen…
Long Range Graph Benchmark
Vijay Prakash Dwivedi, Ladislav Rampášek, Mikhail Galkin +4
Graph Neural Networks (GNNs) that are based on the message passing (MP) paradigm generally exchange information between 1-hop neighbors to build node representations at each layer.…
A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs
Zhaocheng Zhu, Xinyu Yuan, Mikhail Galkin +4
Reasoning on large-scale knowledge graphs has been long dominated by embedding methods. While path-based methods possess the inductive capacity that embeddings lack, their scalabil…
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi +3
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. Graph…
Neural-Symbolic Models for Logical Queries on Knowledge Graphs
Zhaocheng Zhu, Mikhail Galkin, Zuobai Zhang +1
Answering complex first-order logic (FOL) queries on knowledge graphs is a fundamental task for multi-hop reasoning. Traditional symbolic methods traverse a complete knowledge grap…
A Unified Framework for Rank-based Evaluation Metrics for Link Prediction in Knowledge Graphs
Charles Tapley Hoyt, Max Berrendorf, Mikhail Galkin +2
The link prediction task on knowledge graphs without explicit negative triples in the training data motivates the usage of rank-based metrics. Here, we review existing rank-based m…
An Open Challenge for Inductive Link Prediction on Knowledge Graphs
Mikhail Galkin, Max Berrendorf, Charles Tapley Hoyt
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 ta…