启发式自定义相似度索引(HCSI):一种用于链接预测的新型机器学习方法

IF 3.7 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Paraskevas Dimitriou, Vasileios Karyotis
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引用次数: 0

摘要

链路预测是网络分析中的一项基本任务,旨在预测网络中节点之间缺失或未来的连接。随着社会网络、生物系统、互联网和科学协作网络等领域中复杂网络数据的日益可用性,准确的链接预测方法变得越来越重要。邻域或基于图的链路预测算法适用于不同类型的网络,因此不能有效地利用网络结构上的差异。由于每个领域的独特特征,基于机器或深度学习的链路预测算法适用于每种网络的类型不同,但通常情况下,大多数算法给出的结果都很差。在本文中,我们提出了一种新的链接预测方法,利用机器学习和进化算法的力量。我们的方法利用本地网络信息,通过启发式机器学习架构将网络拓扑编码为链接嵌入。我们引入了一种新的工具,可以有效地从网络结构中提取特征,并通过一种进化算法将它们有效地组合在一起,从而提高链接嵌入的判别能力。我们在11个基准数据集上评估了我们的方法,并与一系列(总共11个)有效和最先进的算法相比,证明了它的优越性能。我们的方法推进了最先进的链路预测,在我们应用它的所有网络中产生比其他方法更好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Heuristic Custom Similarity Index (HCSI): A novel machine learning approach for link prediction
Link prediction is a fundamental task in network analysis, aiming at predicting missing or future connections between nodes in a network. With the growing availability of complex network data in fields like social networks, biological systems, the Internet, and scientific collaboration networks, accurate link prediction methods are becoming increasingly critical. Neighborhood or graph based link prediction algorithms are applied identically to different types of networks so that any differences in their structures are not exploited efficiently. Machine or deep learning based link prediction algorithms apply to each kind of network differently depending on the type of network, due to the unique characteristics of each domain, but frequently, most of them give poor results. In this paper, we propose a novel approach for link prediction, leveraging the power of machine learning and evolutionary algorithms. Our method utilizes local network information by encoding the network topology into link embeddings through a heuristic machine learning architecture. We introduce a novel tool to extract features from network structure effectively and combine them in an effective way through an evolutionary algorithm improving the discriminative power of link embeddings. We evaluate our method on eleven benchmark datasets and demonstrate its superior performance compared to a series (eleven in total) of effective and state-of-the-art algorithms. Our approach advances the state-of-the-art in link prediction yielding better results than other methods in all the networks we have applied it to.
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来源期刊
Journal of Computational Science
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).
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