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引用次数: 0
摘要
复杂网络中关键节点的识别是许多网络科学领域的重要课题。它对病毒营销、流行病传播和影响力最大化等各种现实应用至关重要。近年来,事实证明机器学习算法在准确性和一致性方面优于传统的基于中心性的方法,但这种方法仍需进一步完善。可以从网络中提取哪些关于影响者的信息?如何精确获取训练所需的标签?这些模型能否很好地泛化?在本文中,我们针对影响力传播问题提出了一个基于机器学习的增强型框架,从而回答了这些问题。我们的重点是识别独立级联模型的关键节点,这是一种流行的参考方法。我们的主要贡献在于通过引入 "智能箱 "改进了获取训练所需标签的过程,并证明了其相对于已知方法的优势。接下来,我们展示了我们的方法允许 ML 模型不仅预测给定节点的影响,还能确定传播过程的其他特征--这是相关文献的另一个新颖之处。最后,我们广泛测试了我们的框架及其在不同类型和规模的复杂网络中的推广能力,从而对这些方法的特性有了重要的了解。
Identifying Key Nodes for the Influence Spread Using a Machine Learning Approach.
The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing "Smart Bins" and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process-which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods.
期刊介绍:
Entropy (ISSN 1099-4300), an international and interdisciplinary journal of entropy and information studies, publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish as much as possible their theoretical and experimental details. There is no restriction on the length of the papers. If there are computation and the experiment, the details must be provided so that the results can be reproduced.