[论文解读] Network Community Detection: A Review and Visual Survey

Bisma S. Khan, Muaz A. Niazi|arXiv (Cornell University)|Aug 3, 2017

Complex Network Analysis Techniques参考文献 78被引用 80

一句话总结

本论文使用 CiteSpace 对网络社区检测文献进行可视化科学计量综述，映射趋势、影响力作者、期刊和机构，并强调关键枢纽和高被引节点。

ABSTRACT

Community structure is an important area of research. It has received a considerable attention from the scientific community. Despite its importance, one of the key problems in locating information about community detection is the diverse spread of related articles across various disciplines. To the best of our knowledge, there is no current comprehensive review of recent literature which uses a scientometric analysis using complex networks analysis covering all relevant articles from the Web of Science (WoS). Here we present a visual survey of key literature using CiteSpace. The idea is to identify emerging trends besides using network techniques to examine the evolution of the domain. Towards that end, we identify the most influential, central, as well as active nodes using scientometric analyses. We examine authors, key articles, cited references, core subject categories, key journals, institutions, as well as countries. The exploration of the scientometric literature of the domain reveals that Yong Wang is a pivot node with the highest centrality. Additionally, we have observed that Mark Newman is the most highly cited author in the network. We have also identified that the journal, "Reviews of Modern Physics" has the strongest citation burst. In terms of cited documents, an article by Andrea Lancichinetti has the highest centrality score. We have also discovered that the origin of the key publications in this domain is from the United States. Whereas Scotland has the strongest and longest citation burst. Additionally, we have found that the categories of "Computer Science" and "Engineering" lead other categories based on frequency and centrality respectively.

研究动机与目标

• 推动对网络社区检测文献的全面、跨学科综合。
• 通过科学计量视角识别新兴趋势及领域演变。
• 揭示该领域的关键作者、文章、引用文献、期刊、机构以及地理分布格局。

提出的方法

• 对网络社区检测的 Web of Science 文献应用基于 CiteSpace 的可视化调研。
• 计算科学计量指标，如中心性、引文爆发和引用次数，以识别枢纽节点和具有影响力的项。
• 分析作者、文章、被引文献、核心学科分类和地理分布。

实验结果

研究问题

• RQ1 在网络社区检测文献中，谁是最具影响力和中心地位的参与者（作者、文章、期刊）？
• RQ2 推动该领域的主要出版渠道、国家和学科领域有哪些？
• RQ3 哪些工作展现出显著的引文爆发或中心性，表明新兴或持久的影响力？
• RQ4 该领域起源于何处，地理和学科上又有哪些演变？

主要发现

• Yong Wang 被识别为具有最高中心性的枢纽节点。
• Mark Newman 是网络中被引用次数最高的作者。
• 期刊 Reviews of Modern Physics 显示出最强的引文爆发。
• Andrea Lancichinetti 的工作在被引文献中具有最高的中心性。
• 关键出版物的起源主要在美国，而苏格兰显示出最强和最长的引文爆发。
• 核心类别 Cited：Computer Science 在频率上领先，Engineering 在中心性上领先。