Extending Graph Neural Networks with Relational Logic

This thesis builds upon the principles of Graph Neural Networks and extends them further by coupling with concepts from relational logic to ...

Relational Neural Networks: Redefining Graph Databases for ...

This systems thinking naturally extended into my exploration of AI. Through tools like ChatGPT, I began learning not just how to prompt AI but ...

Beyond graph neural networks with lifted relational neural networks

We show how to elegantly capture the core information propagation principles of GNNs with relational logic, extend it into some of the most ...

[2007.06286] Beyond Graph Neural Networks with Lifted Relational ...

... Networks, where small parameterized logic programs are used to encode relational learning scenarios. ... extended towards higher relational ...

Beyond Graph Neural Networks with Lifted Relational Neural ... - arXiv

There is a number of works targeting similar abilities by extending logic programming with numeri- cal parameters. The most prominent ...

Extending Graph Neural Networks with Global Features - reposiTUm

Brasoveanu et al., Extending Graph Neural Networks with Global Features (Extended Abstract). Presented at the Second Learning on Graphs Conference (LoG 2023), ...

Graph Representation Learning on Relational Databases

(c) Relational data is transformed into its Relational Entity Graph, and a Graph Neural Network is trained over the graph with the supervision provided by the ...

Relgraph: A Multi-Relational Graph Neural Network Framework for ...

We introduce the Relgraph, a novel knowledge graph reasoning framework that explores logical relationships between relations by introducing a relation graph.

Beyond Graph Neural Networks with PyNeuraLogic | by Gustav Šír

Differentiable logic programming in Python for elegant encoding and extending of GNNs towards more complex deep relational models.

Link prediction for knowledge graphs based on extended relational ...

On the other hand, neural network-based models first learn the knowledge representation of entities and relations in the graph, and then compute ...

Extending the Design Space of Graph Neural Networks by ...

graph, and thus equivalent to the relational pooling on graph [15]. It is ... Lecture Notes in Logic. Cambridge University Press, 2017. doi: 10.1017 ...

Link Prediction with Relational Hypergraphs | OpenReview

... Learning for Multi-relational Graph Neural Networks, LOG 2023. [3]Fu ... logical reasoning ability of HCNet is same as grade model logic on ...

Relational Data Imputation with Graph Neural Networks

We evaluated GRIMP against six repre- sentative baselines, and we develop one additional baseline by extending one of them to handle external information.

Meta-Path Learning for Multi-relational Graph Neural Networks

RGCN layer The relational graph convolutional layer from [25] extends the standard convolution operation on graphs [10] to the multi-relational setting by ...

Beyond Graph Neural Networks with Lifted Relational ... - YouTube

... Relational Neural Networks, where small parameterized logic programs are used to encode relational learning scenarios. Original paper: https ...

Evaluating Logical Generalization in Graph Neural Networks

(c):. Learning representations of the relations (r) using fr with the extended graph as the input. In case of Param models, the relation representations are ...

Beyond graph neural networks with lifted relational neural networks

When presented with relational data, such as various forms of graphs, the logic program interpreter dynamically unfolds differentiable computation graphs to be ...

TSI-GNN: Extending Graph Neural Networks to Handle Missing Data ...

While our application of the reshaping operation is for graph representation, the logic remains the same. Thus, this joint bipartite graph captures temporal ...

Combinatorial Optimization and Reasoning with Graph Neural ...

However, recent years have seen a surge of interest in using machine learning, especially graph neural networks. (GNNs), as a key building block for ...

Deep Learning with Relational Logic Representations - Gustav Šír

2 We later exploit the fact that relational logic is not limited to graphs while generalizing Graph Neural Networks in ... While this can be done by extending the ...