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图聚集技术的现状与挑战 被引量：6

Progress and Challenges of Graph Aggregation and Summarization Techniques
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摘要 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. 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.
作者 潘秋萍 游进国 张志朋 董朋志 胡宝丽
机构地区 昆明理工大学信息工程与自动化学院
出处 《软件学报》 EI CSCD 北大核心 2015年第1期167-177,共11页 Journal of Software
基金 国家自然科学基金(61462050) 云南省自然科学基金(2013FZ020 201303095) 云南省教育厅科学研究基金重点项目(2013Z125) 高等学校学科创新引智计划(111计划)(B12028)
关键词 图数据 图聚集 分组标准 属性信息一致 结构信息一致 graph data graph aggregation grouping criteria consistency of attribute information consistency of structure information
分类号 TP311 [自动化与计算机技术—计算机软件与理论]
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参考文献32

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二级参考文献18

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