Node Embedding Preserving Graph Summarization

IF 4 3区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

ACM Transactions on Knowledge Discovery from Data Pub Date : 2024-03-08 DOI:10.1145/3649505

Houquan Zhou, Shenghua Liu, Huawei Shen, Xueqi Cheng

引用次数: 0

Abstract

Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to preserve original node embeddings encoding rich structural information of nodes on the summary graph. However, their algorithms are designed heuristically and not theoretically guaranteed. In this paper, we theoretically study the problem of preserving node embeddings on summary graph. We prove that three matrix-factorization based node embedding methods of the original graph can be approximated by that of the summary graph, and propose a novel graph summarization method, named HCSumm, based on this analysis. Extensive experiments are performed on real-world datasets to evaluate the effectiveness of our proposed method. The experimental results show that our method outperforms the state-of-the-art methods in preserving node embeddings.

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节点嵌入保存图摘要

图摘要是分析大规模图的有用工具。一些研究试图在摘要图上保留原始节点嵌入，以编码节点的丰富结构信息。然而，它们的算法都是启发式设计的，并没有理论保证。本文从理论上研究了在摘要图上保留节点嵌入的问题。我们证明了原始图的三种基于矩阵因子化的节点嵌入方法可以被摘要图的节点嵌入方法近似，并在此基础上提出了一种名为 HCSumm 的新型图摘要方法。我们在真实世界的数据集上进行了广泛的实验，以评估我们提出的方法的有效性。实验结果表明，我们的方法在保留节点嵌入方面优于最先进的方法。

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

ACM Transactions on Knowledge Discovery from Data COMPUTER SCIENCE, INFORMATION SYSTEMS-COMPUTER SCIENCE, SOFTWARE ENGINEERING

CiteScore

6.70

自引率

5.60%

发文量

172

审稿时长

3 months

期刊介绍： TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.