发表机构
Blue Yonder India Pvt. Ltd.; Centre for Machine Intelligence and Data Science, Indian Institute of Technology Bombay; Dept. of Computer Science and Engineering, National Institute of Technology Tiruchirappalli; Dept. of Computer Science and Engineering, Indian Institute of Technology Kanpur; TechSoftX Corporation（Blue Yonder India Pvt. Ltd.; 数据科学中心，印度理工学院班加罗尔; 计算机科学与工程系，国家理工学院特里奇里帕利; 计算机科学与工程系，印度理工学院坎pur; TechSoftX公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对如何生成可配置且支持多查询的图摘要问题，提出了CGS框架，它基于聚合公共邻域节点的思想，由三个摘要变体组成，通过引入邻域损失容忍阈值限制重建损失，实验证明其摘要性能优于现有方法，能高效准确回答图查询。
AI 中文摘要
给定一个大型图，如何生成一个可由用户配置并支持多个图查询且无损失或高精度的紧凑摘要图？图数据集规模不断增大，使上述图摘要问题变得非常相关。现有方法存在局限，不存在能提供高压缩率、支持对摘要图进行多查询且精度高，并允许用户基于无损或有损摘要、可容忍邻域损失量、可容忍损失类型（误报边、漏报边或两者皆无）进行配置的可配置图摘要方法。为克服这些局限，我们提出了一种新颖的图摘要框架CGS。它基于聚合具有公共邻域的节点的思想，由CGS - E、CGS - I和CGS - U三个摘要变体组成，CGS - E是无损方案，CGS - I和CGS - U是有损方案，分别允许重建无误报边和无漏报边的输入图。为限制图重建损失，引入用户指定的参数邻域损失容忍阈值。实验表明，CGS比现有方法具有更优的摘要性能，能以较高的精度和效率回答图查询。
英文摘要
Given a large graph, how to generate a compact summary graph that is configurable by the user and supports multiple graph queries with either no loss or with high accuracy? The ever growing size of graph datasets makes the above question on graph summarization very pertinent. Although, there are several approaches, there does not exist a configurable graph summarization method that offers high compression along with support for multiple graph queries on the summary graph with high accuracy, and allows the user to configure the summarization based on: (1) lossless or lossy summarization, (2) amount of tolerable neighborhood loss, (3) the type of loss it can tolerate, in terms of false positive edges (i.e., extra edges), false negative edges (i.e., missing edges), or neither, in both the (a) reconstructed graph and the (b) query answers. To overcome these limitations, we propose a novel graph summarization framework CGS (Configurable Graph Summarizer) that builds upon the idea of aggregating nodes with common neighborhoods. The CGS framework consists of three summarization variants, CGS-E, CGS-I and CGS-U. While CGS-E is a lossless scheme, CGS-I and CGS-U are lossy schemes that allow reconstruction of the input graph with no false positive edges and no false negative edges, respectively. To bound the graph reconstruction loss, we introduce a user-specified parameter neighborhood loss tolerance threshold, that limits the maximum loss allowed in the neighborhood of each node. This allows graph reconstruction and neighborhood query evaluation with either no loss or with bounded loss guarantees. Empirical evaluation on several synthetic and real-world graphs shows that CGS offers superior summarization than the state-of-the-art methods, and can answer graph queries with fairly high accuracy and efficiency.
