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   Articles with "graph neural" as a keyword



Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks: A MELD Study.

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recommendations! Published in 2025 at "JAMA neurology"

DOI: 10.1001/jamaneurol.2024.5406

Abstract: Importance A leading cause of surgically remediable, drug-resistant focal epilepsy is focal cortical dysplasia (FCD). FCD is challenging to visualize and often considered magnetic resonance imaging (MRI) negative. Existing automated methods for FCD detection are… read more here.

Keywords: detection; sensitivity specificity; graph neural; epilepsy ... See more keywords

An Explainable Graph Neural Framework to Identify Cancer‐Associated Intratumoral Microbial Communities

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recommendations! Published in 2024 at "Advanced Science"

DOI: 10.1002/advs.202403393

Abstract: Microbes are extensively present among various cancer tissues and play critical roles in carcinogenesis and treatment responses. However, the underlying relationships between intratumoral microbes and tumors remain poorly understood. Here, a MIcrobial Cancer‐association Analysis using… read more here.

Keywords: cancer; framework; graph neural; microbial communities ... See more keywords

Graph Decipher: A transparent dual‐attention graph neural network to understand the message‐passing mechanism for the node classification

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recommendations! Published in 2022 at "International Journal of Intelligent Systems"

DOI: 10.1002/int.22966

Abstract: Graph neural networks (GNNs) can be effectively applied to solve many real‐world problems across widely diverse fields. Their success is inseparable from the message‐passing mechanisms evolving over the years. However, current mechanisms treat all node… read more here.

Keywords: node classification; message passing; graph; graph neural ... See more keywords

Mfgnn: Multi‐Scale Feature‐Attentive Graph Neural Networks for Molecular Property Prediction

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recommendations! Published in 2025 at "Journal of Computational Chemistry"

DOI: 10.1002/jcc.70011

Abstract: In the realm of artificial intelligence‐driven drug discovery (AIDD), accurately predicting the influence of molecular structures on their properties is a critical research focus. While deep learning models based on graph neural networks (GNNs) have… read more here.

Keywords: chemistry; scale feature; graph neural; multi scale ... See more keywords

LatticeGraphNet: a two-scale graph neural operator for simulating lattice structures

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recommendations! Published in 2024 at "Engineering with Computers"

DOI: 10.1007/s00366-024-02034-7

Abstract: This study introduces a two-scale graph neural operator (GNO), namely, LatticeGraphNet (LGN), designed as a surrogate model for costly nonlinear finite-element simulations of three-dimensional latticed parts and structures. LGN has two networks: LGN-i, learning the… read more here.

Keywords: operator; graph neural; latticegraphnet two; two scale ... See more keywords

A review of hyperspectral image classification based on graph neural networks

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recommendations! Published in 2025 at "Artificial Intelligence Review"

DOI: 10.1007/s10462-025-11169-y

Abstract: Hyperspectral images provide rich spectral-spatial information but pose significant classification challenges due to high dimensionality, noise, mixed pixels, and limited labeled samples. Graph Neural Networks (GNNs) have emerged as a promising solution, offering a semi-supervised… read more here.

Keywords: classification; image classification; hyperspectral image; graph neural ... See more keywords
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Nation-wide human mobility prediction based on graph neural networks

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recommendations! Published in 2021 at "Applied Intelligence"

DOI: 10.1007/s10489-021-02645-3

Abstract: Nowadays, the anticipation of human mobility flow has important applications in many domains ranging from urban planning to epidemiology. Because of the high predictability of human movements, numerous successful solutions to perform such forecasting have… read more here.

Keywords: mobility; graph neural; human mobility; nation wide ... See more keywords

SPA: A poisoning attack framework for graph neural networks through searching and pairing

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recommendations! Published in 2025 at "Machine Learning"

DOI: 10.1007/s10994-024-06706-9

Abstract: Graph Neural Networks (GNN) have played an important role in many fields, while GNNs also suffer from adversarial attacks that aim to malfunction the GNN model by changing the adjacency matrix (i.e. generating adversarial edges)… read more here.

Keywords: attack; adversarial edges; graph neural; poisoning attack ... See more keywords

SGNNRec: A Scalable Double-Layer Attention-Based Graph Neural Network Recommendation Model

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recommendations! Published in 2024 at "Neural Processing Letters"

DOI: 10.1007/s11063-024-11555-7

Abstract: Due to the information from the multi-relationship graphs is difficult to aggregate, the graph neural network recommendation model focuses on single-relational graphs (e.g., the user-item rating bipartite graph and user-user social relationship graphs). However, existing… read more here.

Keywords: information; graph neural; model; recommendation ... See more keywords

DialGNN: Heterogeneous Graph Neural Networks for Dialogue Classification

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recommendations! Published in 2024 at "Neural Processing Letters"

DOI: 10.1007/s11063-024-11595-z

Abstract: Dialogue systems have attracted growing research interests due to its widespread applications in various domains. However, most research work focus on sentence-level intent recognition to interpret user utterances in dialogue systems, while the comprehension of… read more here.

Keywords: heterogeneous graph; classification; dialgnn heterogeneous; graph neural ... See more keywords

Design your own universe: a physics-informed agnostic method for enhancing graph neural networks

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recommendations! Published in 2024 at "International Journal of Machine Learning and Cybernetics"

DOI: 10.1007/s13042-024-02326-w

Abstract: Physics-informed Graph Neural Networks have achieved remarkable performance in learning through graph-structured data by mitigating common GNN challenges such as over-smoothing, over-squashing, and heterophily adaption. Despite these advancements, the development of a simple yet effective… read more here.

Keywords: design universe; method; physics informed; graph neural ... See more keywords