Inductive knowledge graph completion has been considered as the task of predicting
missing triplets between new entities that are not observed during training. While most …

Recently, many works studied the expressive power of graph neural networks (GNNs) by
linking it to the $1 $-dimensional Weisfeiler-Leman algorithm ($1\text {-}\mathsf {WL} $) …

Abstract Graph Neural Networks (GNNs) have become the de-facto standard tool for
modeling relational data. However, while many real-world graphs are directed, the majority …

Graph Neural Networks (GNNs) are popular models for machine learning on graphs that
typically follow the message-passing paradigm, whereby the feature of a node is updated …

Complex logical query answering (CLQA) is a recently emerged task of graph machine
learning that goes beyond simple one-hop link prediction and solves a far more complex …

Graph neural networks are prominent models for representation learning over graph-
structured data. While the capabilities and limitations of these models are well-understood …

Abstract The Weisfeiler-Lehman (WL) test is a fundamental iterative algorithm for checking
the isomorphism of graphs. It has also been observed that it underlies the design of several …

Recent research has shown that alignment between the structure of graph data and the
geometry of an embedding space is crucial for learning high-quality representations of the …

Seminal research in the field of graph neural networks (GNNs) has revealed a direct
correspondence between the expressive capabilities of GNNs and the $ k $-dimensional …

Complex logical query answering (CLQA) in knowledge graphs (KGs) goes beyond simple
KG completion and aims at answering compositional queries comprised of multiple …