图聚集技术的现状与挑战

图聚集技术旨在获取能够涵盖原图大部分信息的简洁超图,用于提炼概要信息、解决存储消耗和社交隐私保护等问题.对当前的图聚集技术进行研究,综述了现有图聚集技术中的分组方法并对其进行分类,将分组标准划分为基于属性一致性、基于邻接分组一致性、基于关联强度一致性、基于邻接顶点一致性和基于零重建误差这5类;在高层次上将各分组标准概括为基于属性、基于结构和同时基于属性和结构的图聚集.较为全面地总结和分析了当前图聚集技术的研究现状和进展,并探讨了未来研究的方向.

Progress and Challenges of Graph Aggregation and Summarization Techniques

Graph aggregation and summarization is to obtain a concise supergraph covering the most information of the underlying input graph, and it is used to extract summarization, solve storage consumption and protect privacy in social networks. This paper investigates current graph aggregation and summarization techniques and further reviews and classifies their partitioning/grouping methods. Based on the consistency of grouping information, five grouping criteria are specified: The consistency of attribute information, the consistency of neighborhood group, the consistency of connection strength, the consistency of neighborhood vertex and reconstruction zero error. From the top level view, graph aggregation and summarization techniques can be classified into three types, namely, attribute similarity, structure cohesiveness and the hybrid of both. This paper comprehensively summarizes the state of art of current research works, and explores the research directions in the future.