面向同构多层网络的可扩展子结构发现算法
Scalable Substructure Discovery Algorithm For Homogeneous Multilayer Networks
AI总结：
本文提出一种基于解耦方法和Map/Reduce范式的同构多层网络可扩展子结构发现算法，通过独立处理各层再组合结果识别子结构，并在大规模合成与真实数据集上验证了其正确性、加速比与响应时间。
摘要：
图挖掘通过分析真实世界图，在图建模的应用中寻找核心子结构（连通子图）。子结构发现是一个在大型数据集中识别有意义的模式、结构或组件的过程。这些子结构可以有多种类型，如频繁模式、模体或数据中的其他相关特征。为了对具有多种实体和关系类型的复杂数据集进行建模，已证明多层网络（或 MLNs）比简单图和属性图更有效。已证明使用解耦方法在 MLNs 上的分析算法既高效又准确。因此，本文专注于使用一种新颖的基于解耦的方法在同构多层网络（一种 MLN）中进行子结构发现。在这种方法中，每层被独立处理，然后将来自两层或多层的结果组合起来，以识别整个 MLN 中的子结构。该算法的设计与实现（包括组合部分）使用了分布式处理框架之一（Map/Reduce 范式）以提供可扩展性。在确立正确性后，我们在具有多样图特征的大型合成和真实数据集上，通过广泛的实验分析评估了所提算法和方法的加速比与响应时间。
英文摘要：
Graph mining analyzes real-world graphs to find core substructures (connected subgraphs) in applications modeled as graphs. Substructure discovery is a process that involves identifying meaningful patterns, structures, or components within a large data set. These substructures can be of various types, such as frequent patterns, motifs, or other relevant features within the data. To model complex data sets -- with multiple types of entities and relationships -- multilayer networks (or MLNs) have been shown to be more effective as compared to simple and attributed graphs. Analysis algorithms on MLNs using the decoupling approach have been shown to be both efficient and accurate. Hence, this paper focuses on substructure discovery in homogeneous multilayer networks (one type of MLN) using a novel decoupling-based approach. In this approach, each layer is processed independently, and then the results from two or more layers are composed to identify substructures in the entire MLN. The algorithm is designed and implemented, including the composition part, using one of the distributed processing frameworks (the Map/Reduce paradigm) to provide scalability. After establishing the correctness, we analyze the speedup and response time of the proposed algorithm and approach through extensive experimental analysis on large synthetic and real-world data sets with diverse graph characteristics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
