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IEEE Transactions on Computational Biology and Bioinformatics
The study of the relationship between circular RNA (circRNA) and disease is crucial for understanding the mechanisms underlying disease onset. However, relying on biological experiments to explore all potential connections between circRNAs and diseases is both time-consuming and labor-intensive. While various prediction methods have been proposed, they
Pengli Lu, Xusheng Liu, Fentang Gao
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The study of the relationship between circular RNA (circRNA) and disease is crucial for understanding the mechanisms underlying disease onset. However, relying on biological experiments to explore all potential connections between circRNAs and diseases is both time-consuming and labor-intensive. While various prediction methods have been proposed, they
Pengli Lu, Xusheng Liu, Fentang Gao
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DepthGraphNet: Circuit Graph Isomorphism Detection via Siamese-Graph Neural Networks
2023 ACM/IEEE 5th Workshop on Machine Learning for CAD (MLCAD), 2023Fin Amin +2 more
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A knowledge-enhanced directed graph isomorphism network for multimodal sarcasm detection
The Electronic LibraryPurpose Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features for multimodal information effectively. To address these limitations, this study proposes a novel multimodal sarcasm detection model based on the directed graph ...
Yu Liu, Ziming Zeng
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On the Identification of Isomorphic Graphs for Graph Neural Network using Multi-graph Approach
2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2022Adrien Njanko, Danda B. Rawat
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Graph Isomorphism Networks for Wireless Link Layer Anomaly Classification
2023 IEEE Wireless Communications and Networking Conference (WCNC), 2023Blaz Bertalanic, Carolina Fortuna
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Graph isomorphism network with weighted multi‐aggregators for building shape classification
Transactions in GISAbstractBuilding shape cognition is essential for tasks, such as map generalization, urban modeling, and building semantics and distribution pattern recognition. Traditional geometric and statistical methods rely on human‐defined shape indicators, and spectral‐based graph neural networks (GNNs) require Laplacian eigendecomposition, resulting in high ...
Ya Zhang +5 more
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Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Stefanos Zafeiriou, Giorgos Bouritsas
exaly
Kernel entropy graph isomorphism network for graph classification
Pattern RecognitionLixiang Xu +4 more
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