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Found 15 papers, 7 papers with code
Graph neural network outputs are almost surely asymptotically constant
Our results apply to a broad class of random graph models, including the (sparse) Erd\H{o}s-R\'enyi model and the stochastic block model.
Link Prediction with Relational Hypergraphs
In this paper, we propose two frameworks for link prediction with relational hypergraphs and conduct a thorough analysis of the expressive power of the resulting model architectures via corresponding relational Weisfeiler-Leman algorithms, and also via some natural logical formalisms.
Future Directions in Foundations of Graph Machine Learning
Machine learning on graphs, especially using graph neural networks (GNNs), has seen a surge in interest due to the wide availability of graph data across a broad spectrum of disciplines, from life to social and engineering sciences.
Cooperative Graph Neural Networks
Graph neural networks are popular architectures for graph machine learning, based on iterative computation of node representations of an input graph through a series of invariant transformations.
PlanE: Representation Learning over Planar Graphs
Graph neural networks are prominent models for representation learning over graphs, where the idea is to iteratively compute representations of nodes of an input graph through a series of transformations in such a way that the learned graph function is isomorphism invariant on graphs, which makes the learned representations graph invariants.
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Our goal is to provide a systematic understanding of the landscape of graph neural networks for knowledge graphs pertaining to the prominent task of link prediction.
Shortest Path Networks for Graph Property Prediction
Most graph neural network models rely on a particular message passing paradigm, where the idea is to iteratively propagate node representations of a graph to each node in the direct neighborhood.
Equivariant Quantum Graph Circuits
We investigate quantum circuits for graph representation learning, and propose equivariant quantum graph circuits (EQGCs), as a class of parameterized quantum circuits with strong relational inductive bias for learning over graph-structured data.
Temporal Knowledge Graph Completion using Box Embeddings
Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Knowledge Graph Embedding
Temporal Knowledge Graph Completion
Node Classification Meets Link Prediction on Knowledge Graphs
Node classification and link prediction are widely studied in graph representation learning.
The Surprising Power of Graph Neural Networks with Random Node Initialization
In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties.
BoxE: A Box Embedding Model for Knowledge Base Completion
Knowledge base completion (KBC) aims to automatically infer missing facts by exploiting information already present in a knowledge base (KB).
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On the Approximability of Weighted Model Integration on DNF Structures
Weighted model counting (WMC) consists of computing the weighted sum of all satisfying assignments of a propositional formula.
Dynamic Bayesian Ontology Languages
Many formalisms combining ontology languages with uncertainty, usually in the form of probabilities, have been studied over the years.
Adding Context to Knowledge and Action Bases
Knowledge and Action Bases (KABs) have been recently proposed as a formal framework to capture the dynamics of systems which manipulate Description Logic (DL) Knowledge Bases (KBs) through action execution.
