Edge-Wise Graph-Instructed Neural Networks

IF 3.1 3区计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Computational Science Pub Date : 2025-02-01 DOI:10.1016/j.jocs.2024.102518

Francesco Della Santa , Antonio Mastropietro , Sandra Pieraccini , Francesco Vaccarino

引用次数: 0

Abstract

The problem of multi-task regression over graph nodes has been recently approached through Graph-Instructed Neural Network (GINN), which is a promising architecture belonging to the subset of message-passing graph neural networks. In this work, we discuss the limitations of the Graph-Instructed (GI) layer, and we formalize a novel edge-wise GI (EWGI) layer. We discuss the advantages of the EWGI layer and we provide numerical evidence that EWGINNs perform better than GINNs over some graph-structured input data, like the ones inferred from the Barabási-Albert graph, and improve the training regularization on graphs with chaotic connectivity, like the ones inferred from the Erdos–Rényi graph.

Abstract Image

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边缘图指示神经网络

图指示神经网络（GINN）是一种很有前途的结构，属于消息传递图神经网络的子集，近年来研究了图节点上的多任务回归问题。在这项工作中，我们讨论了图指示（GI）层的局限性，并形式化了一种新的沿边GI （EWGI）层。我们讨论了EWGI层的优点，并提供了数值证据，证明ewginn在一些图结构的输入数据上比ginn表现得更好，比如从Barabási-Albert图中推断出来的数据，并改善了对具有混沌连通性的图的训练正则化，比如从erdos - r nyi图中推断出来的图。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

5.50

自引率

3.00%

发文量

227

审稿时长

41 days

期刊介绍： Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).