Extending Graph Neural Networks with Relational Logic

Logical Distillation of Graph Neural Networks - Pascal Welke

The key motivation for our model is the close relationship between GNNs and first-order logic with only two variables and counting quantifiers C2 (Barceló et al ...

Graph neural network - Wikipedia

A graph neural network (GNN) belongs to a class of artificial neural networks for processing data that can be represented as graphs.

A comparison of statistical relational learning and graph neural ...

Inference is then performed on the ground model. Probabilistic soft logic (Bach et al., 2017), Markov logic networks (Richardson & Domingos, ...

Graph Neural Networks: A Review of Methods and Applications - CDN

Abstract—Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling.

KNOWLEDGE REASONING WITH GRAPH NEURAL NETWORKS A ...

... logic reasoning, seeking to combine the advantages of relational ... 104] extend ... in logic reasoning and graph neural networks in graph representation learning.

Lifted Relational Neural Networks - CEUR-WS

By combining relational logic with deep neural networks, we obtain a framework ... For example, extension to multi-instance learning ... The graph neural network ...

Echo state graph neural networks with analogue random resistive ...

1d (see Extended Data Fig. 1 for the stochasticity of dielectric breakdown voltages). Compared with pseudo random number generation using ...

Learning on graphs with logic and neural networks

locality of first-order logic directly extends to relational structures by computing the distances and neighborhoods in the Gaifman graph. Thus, all steps ...

Recurrent Graph Neural Networks and Their Connections to ...

relational) graph neural network classifier ((Col, )-GNN) < ... (multi-)modal logic (or, equivalently, the description logic ... the structure extending A by ...

naganandy/graph-based-deep-learning-literature - GitHub

Interpretable and Generalizable Graph Neural Networks via Subgraph Multilinear Extension ... FL-GNN: A Fuzzy-logic Graph Neural Network · Guided ... Beyond Graphs: ...

Extended study on atomic featurization in graph neural networks for ...

The classical machine learning methods that were used to find the relationship between the chemical structure of molecules and their properties ...

An Overview of Graph Models | Papers With Code

Recently, deep learning approaches are being extended to work on graph-structured data, giving rise to a series of graph neural networks addressing different ...

Deep Learning and the Relational Algebra - YouTube

Do we get any benefit by extending the relational algebra with tensor ... Factorized Graph Neural Networks (With Some Algebraic Cheating).

Recurrent Graph Neural Networks and Their Connections to ...

Indeed, labelled graphs, which (trained) GNNs take as in- put, can be seen as relational structures, on which logical formulas are evaluated; furthermore both ...

Graph Neural Networks for Multi-Relational Data

This article describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encode the structure of Knowledge Graphs ...

Graph Neural Networks: Extending Deep Learning to ... - Medium

GNNs are a type of neural network designed to work with graph-structured data. Traditional neural networks, like feedforward or convolutional ...

Tutorial 7: Graph Neural Networks — UvA DL Notebooks v1.2 ...

proposed to extend it to multiple heads similar to the Multi-Head Attention block in Transformers. This results in N attention layers being applied in parallel.

Modeling Semantics with Gated Graph Neural Networks for ...

(2015) and their extension in Bao et al. (2016), the key difference being that we do not differentiate between the core relation and modifiers, but rather allow ...

The Logical Expressiveness of Graph Neural Networks

... Logic 2 · Paul Tarau. 2022. Graph Neural Networks share with Logic Programming several key relational inference mechanisms. ... logic extended with counting ...

Survey of Graph Neural Networks and Applications - Liang - 2022

We then present ways to extend deep learning models to deal with datasets in non-Euclidean space and introduce the GNN-based approaches based on ...