Zobrazeno 1 - 10
of 21 042
pro vyhledávání: '"GNNs"'
Disinformation on social media poses both societal and technical challenges. While previous studies have integrated textual information into propagation networks, they have yet to fully leverage the advancements in Transformer-based language models f
Externí odkaz:
http://arxiv.org/abs/2410.19193
Graph Neural Networks (GNNs) have emerged as powerful models for learning from graph-structured data. However, GNNs lack the inherent semantic understanding capability of rich textual nodesattributes, limiting their effectiveness in applications. On
Externí odkaz:
http://arxiv.org/abs/2410.16822
Graph Neural Networks (GNNs) have shown remarkable success in various graph-based tasks, including node classification, node regression, graph classification, and graph regression. However, their scalability remains a significant challenge, particula
Externí odkaz:
http://arxiv.org/abs/2410.15001
Autor:
Daniëls, Noah, Geerts, Floris
Graph Neural Networks (GNNs) have become an essential tool for analyzing graph-structured data, leveraging their ability to capture complex relational information. While the expressivity of GNNs, particularly their equivalence to the Weisfeiler-Leman
Externí odkaz:
http://arxiv.org/abs/2410.07829
Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Diagnosing AD is challenging due to its heterogeneous nature and variable progression. This study introd
Externí odkaz:
http://arxiv.org/abs/2410.14683
Graph Neural Networks (GNNs) are extensively employed in graph machine learning, with considerable research focusing on their expressiveness. Current studies often assess GNN expressiveness by comparing them to the Weisfeiler-Lehman (WL) tests or cla
Externí odkaz:
http://arxiv.org/abs/2410.01308
Over the past decade, Graph Neural Networks (GNNs) have transformed graph representation learning. In the widely adopted message-passing GNN framework, nodes refine their representations by aggregating information from neighboring nodes iteratively.
Externí odkaz:
http://arxiv.org/abs/2410.01802
Identifying informative low-dimensional features that characterize dynamics in molecular simulations remains a challenge, often requiring extensive hand-tuning and system-specific knowledge. Here, we introduce geom2vec, in which pretrained graph neur
Externí odkaz:
http://arxiv.org/abs/2409.19838