[论文解读] Communities and Hierarchical Organization of Links in Complex Networks

Yong‐Yeol Ahn, James P. Bagrow+1 more|arXiv (Cornell University)|Mar 18, 2009

Bioinformatics and Genomic Networks被引用 24

一句话总结

本文提出了一种新颖的框架，将社区重新定义为链接的集合而非节点的集合，从而在复杂网络中同时检测重叠社区与层次结构。通过聚焦于链接社区，该方法揭示了重叠与层次结构并非相互矛盾，而是同一基础结构的两个方面，该结论在生物网络和社会网络中均得到验证。

ABSTRACT

Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein-protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon.

研究动机与目标

• 解决网络分析中长期存在的重叠社区与层次组织之间难以调和的挑战。
• 克服基于节点的社区检测方法的局限性，该方法无法同时捕捉普遍存在的重叠与全局层次结构。
• 提出一种新范式，即社区由链接而非节点定义，从而实现网络结构的统一表征。

提出的方法

• 将社区重新概念化为链接的聚类，而非节点，从而将关注点从节点成员身份转移至链接的连通性模式。
• 应用一种链接划分算法，根据链接的拓扑相似性及其共享的结构角色对链接进行分组。
• 对链接社区应用层次聚类方法，以揭示多尺度的组织结构。
• 采用类似模块度的度量标准，专门针对链接社区进行优化，以识别具有统计显著性的分组。
• 在真实世界网络中验证该方法，包括蛋白质-蛋白质相互作用网络与代谢网络，以及一个大规模社交网络。
• 证明链接社区在自然容纳重叠的同时，仍能保持跨尺度的层次嵌套结构。

实验结果

研究问题

• RQ1 重叠社区与层次组织是否可以在单一网络表征中同时存在而不产生矛盾？
• RQ2 基于链接的社区如何比基于节点的方法更有效地揭示重叠与层次结构？
• RQ3 在通过链接社区分析时，现实世界网络（如生物网络与社交网络）在多大程度上表现出层次组织？
• RQ4 链接社区能否作为基本构建模块，统一重叠与层次这两大结构原则？
• RQ5 是否存在实证证据表明链接社区在捕捉复杂网络结构方面优于传统的基于节点的社区检测方法？

主要发现

• 链接社区成功调和了重叠社区与层次结构，解决了网络科学中长期存在的张力问题。
• 该方法在主要生物网络中识别出具有层次组织的重叠社区，包括蛋白质-蛋白质相互作用网络与代谢网络。
• 在一个大规模社交网络中，链接社区揭示了从城市内部到区域级别的多尺度社区结构，且普遍存在重叠。
• 该方法揭示出重叠与层次结构并非互斥，而是同一网络组织形式的两个方面。
• 与基于节点的社区检测相比，链接社区提供了更连贯、更全面的网络结构表征。