The article presents a study on the use of Graph Neural Networks (GNNs) for link prediction in heterophilic graphs. The authors introduce GRAFF-LP, an extension to the physics-inspired GRAFF model,…
Continue readingThe article discusses how large language models (LLMs), despite their exceptional applicative potential, have not been effectively used in mining relationships from graph data. The authors propose a framework that…
Continue readingThe authors introduce a new method called Clifford Group Equivariant Simplicial Message Passing Networks. This method combines Clifford group-equivariant layers with simplicial message passing to express geometric features. The method…
Continue readingThe article discusses the development of the Extensible Multi-Granularity Fusion (EMGF) network for Aspect-Based Sentiment Analysis (ABSA). The network integrates information from various sources such as syntactic, attention semantic, and…
Continue readingThe article discusses a new graph knowledge distillation framework, named LinguGKD, using Large Language Models (LLMs) as teacher models and Graph Neural Networks (GNNs) as student models for knowledge distillation….
Continue readingThe study focuses on the challenges of training on large-scale graphs in graph representation learning. It highlights how gradient matching methods, used to condense information-rich synthetic sets, often lead to…
Continue readingThis paper investigates the concept of graph condensation, which is a technique used to reduce the size of large-scale graph datasets without compromising the performance of Graph Neural Networks (GNNs)…
Continue readingThe article delves into the use of graph neural networks (GNN) in dealing with complex relationships and dependencies in graph-structured data. It introduces the concept of graph explanation, which is…
Continue readingThe paper introduces a novel approach to Intrusion Detection Systems (IDS), called X-CBA. This approach leverages the advantages of Graph Neural Networks (GNNs) to effectively process network traffic data while…
Continue readingThe paper presents DeepRicci, a self-supervised model for graph structure-feature co-refinement. It aims to solve the over-squashing issue that is common in Graph Neural Networks (GNNs). This is achieved by…
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