Node importance evaluation in heterogeneous network based on attention mechanism and graph contrastive learning

Neurocomputing, Journal Year: 2025, Volume and Issue: unknown, P. 129555 - 129555

Published: Feb. 1, 2025

Language: Английский

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DCKH‐CNN: A Multimetric Graph‐Based Convolutional Neural Network for Identifying Key Influential Nodes in Earth Surface Data Linked Networks

Transactions in GIS, Journal Year: 2025, Volume and Issue: 29(2)

Published: Feb. 28, 2025

ABSTRACT Identifying key influential nodes in Earth surface data association networks is crucial for optimizing the use of scientific data. However, challenges such as network size, complexity, and dynamic node influence make this task difficult. While deep learning methods have improved recognition accuracy reduced computational costs complex networks, they still struggle with balancing efficiency accuracy. To address this, we propose DCKH‐CNN, a novel Multimetric Graph‐Based Convolutional Neural Network framework. Based on LCNN model, it integrates global local features by calculating metrics degree centrality, K ‐shell, H ‐index, near‐centrality. One‐hop two‐hop adjacency matrices are used to represent internode relationships, enhancing feature representation. Trained small‐scale model captures unique characteristics. Experimental results using SIR demonstrate that DCKH‐CNN surpasses state‐of‐the‐art algorithms vast majority Surface Data Linked (ESSDLN) datasets real‐world accuracy, while demonstrating moderate time consumption. This method offers more efficient approach identifying supporting accurate recommendations intelligent analysis

Language: Английский

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Identifying Influential Nodes Based on Evidence Theory in Complex Network

Entropy, Journal Year: 2025, Volume and Issue: 27(4), P. 406 - 406

Published: April 10, 2025

Influential node identification is an important and hot topic in the field of complex network science. Classical algorithms for identifying influential nodes are typically based on a single attribute or simple fusion few attributes. However, these methods perform poorly real networks with high complexity diversity. To address this issue, new method Dempster–Shafer (DS) evidence theory proposed paper, which improves efficiency through following three aspects. Firstly, quantifies uncertainty its basic belief assignment function combines from different information sources, enabling it to effectively handle uncertainty. Secondly, processes conflicting using Dempster’s rule combination, enhancing reliability decision-making. Lastly, networks, may come multiple dimensions, can integrate multidimensional information. verify effectiveness method, extensive experiments conducted real-world networks. The results show that, compared other algorithms, attacking identified by DS more likely lead disintegration network, indicates that effective key network. further validate algorithm, we use visibility graph algorithm convert GBP futures time series into then rank method. top-ranked correspond peaks troughs series, represents turning points price changes. By conducting in-depth analysis, investors uncover major events influence trends, once again confirming algorithm.

Language: Английский

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Identify influential nodes in directed networks: A neighborhood entropy-based method

Chaos Solitons & Fractals, Journal Year: 2025, Volume and Issue: 197, P. 116444 - 116444

Published: April 27, 2025

Language: Английский

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