锚社区时序网络图生成算法
Generation Algorithm of Temporal Networks with Anchor Communities
郑舒文 1王朝坤1
作者信息
摘要
图数据相关分析任务往往需要合成数据集来检验和评估算法的有效性和高效性.真实世界图数据不仅在拓扑上具有社区结构特征,还往往在时序上呈现出一定的演化特性,社区节点可能在锚定时间窗口内频繁交互.然而,现有合成方法存在一定局限性.大多方法或仅关注网络中的社区结构,或仅关注网络中的时序信息,无法生成节点锚时频繁交互的社区.为克服此局限,提出了锚社区概念及定义以刻画社区内节点锚时频繁交互的特性;接着,基于分布概率生成模型提出了一般时序图生成算法;进一步地,提出了锚社区时序网络图生成算法(GTN-AC),不仅允许用户配置锚定时间窗口,还允许用户指定度数分布和时间戳分布.实验结果表明,相较于基准方法,GTN-AC能在保证较优生成质量的同时拥有较快的生成速度.
Abstract
Algorithms for network analysis tasks require synthetic graph datasets to evaluate their effectiveness and efficiency.Real-world graph data not only possess topological features such as community structures,but also contain temporal information revealing evolutionary semantics.Nodes of real-world communities may interact with each other within a specific anchor time win-dow.However,existing graph generation methods suffer from some limitations.Most of them concentrate on either static commu-nity structures or temporal graphs without community structures,appearing weak in generating communities active during an an-chor time period.To surmount their weakness,this paper introduces the concept of anchor community to depict frequent interac-tions between a group of nodes within an anchor time window.Then it proposes an algorithm to synthesize general temporal net-works based on the distribution probability generation model,and further proposes an efficient generation algorithm of temporal networks with anchor communities(GTN-AC),allowing configuration input such as anchor time windows as well as specified dis-tributions of degree and timestamp.Extensive experimental results indicate that compared with other baseline methods,GTN-AC has a faster generation speed while ensuring preferable generation quality.
关键词
时序网络/锚定时间窗口/锚社区/分布概率生成模型/图生成Key words
Temporal network/Anchor time window/Anchor community/Distribution probability generation model/Graph gene-ration引用本文复制引用
基金项目
国家自然科学基金(62372264)
国家自然科学基金(61872207)
出版年
2024
NETL
NSTL
万方数据