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Granola: Adaptive normalization for graph neural networks
In recent years, significant efforts have been made to refine the design of Graph Neural
Network (GNN) layers, aiming to overcome diverse challenges, such as limited expressive …
Network (GNN) layers, aiming to overcome diverse challenges, such as limited expressive …
[PDF][PDF] Digraf: Diffeomorphic graph-adaptive activation function
In this paper, we propose a novel activation function tailored specifically for graph data in
Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible …
Graph Neural Networks (GNNs). Motivated by the need for graph-adaptive and flexible …
Enhancing High-order Interaction Awareness in LLM-based Recommender Model
Large language models (LLMs) have demonstrated prominent reasoning capabilities in
recommendation tasks by transforming them into text-generation tasks. However, existing …
recommendation tasks by transforming them into text-generation tasks. However, existing …
Graphmaker: Can diffusion models generate large attributed graphs?
Large-scale graphs with node attributes are increasingly common in various real-world
applications. Creating synthetic, attribute-rich graphs that mirror real-world examples is …
applications. Creating synthetic, attribute-rich graphs that mirror real-world examples is …
NT-LLM: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
Graphs are a fundamental data structure for representing relationships in real-world
scenarios. With the success of Large Language Models (LLMs) across various natural …
scenarios. With the success of Large Language Models (LLMs) across various natural …
Motif-driven molecular graph representation learning
Abstract Graph Neural Networks (GNNs) have emerged as powerful tools for molecular
graph analysis. Subgraph-based GNNs focus on learning high-level local patterns beyond …
graph analysis. Subgraph-based GNNs focus on learning high-level local patterns beyond …
Learning Efficient Positional Encodings with Graph Neural Networks
Positional encodings (PEs) are essential for effective graph representation learning because
they provide position awareness in inherently position-agnostic transformer architectures …
they provide position awareness in inherently position-agnostic transformer architectures …
Towards Graph Foundation Models: A Study on the Generalization of Positional and Structural Encodings
Recent advances in integrating positional and structural encodings (PSEs) into graph neural
networks (GNNs) have significantly enhanced their performance across various graph …
networks (GNNs) have significantly enhanced their performance across various graph …
On the Effectiveness of Random Weights in Graph Neural Networks
Graph Neural Networks (GNNs) have achieved remarkable success across diverse tasks on
graph-structured data, primarily through the use of learned weights in message passing …
graph-structured data, primarily through the use of learned weights in message passing …
LASE: Learned Adjacency Spectral Embeddings
We put forth a principled design of a neural architecture to learn nodal Adjacency Spectral
Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method …
Embeddings (ASE) from graph inputs. By bringing to bear the gradient descent (GD) method …