[Paper Review] Network Community Detection: A Review and Visual Survey

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

Complex Network Analysis Techniques78 references80 citations

TL;DR

This paper presents a visual scientometric survey of network community detection literature using CiteSpace to map trends, influential authors, journals, and institutions, highlighting key pivot and highly cited nodes.

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.

Motivation & Objective

• Motivate a comprehensive, cross-disciplinary synthesis of network community detection literature.
• Identify emerging trends and the evolution of the domain through a scientometric lens.
• Reveal key authors, articles, references, journals, institutions, and geographic patterns in the field.

Proposed method

• Apply CiteSpace-based visual survey to Web of Science literature on network community detection.
• Compute scientometric indicators such as centrality, citation bursts, and citation counts to identify pivot nodes and influential items.
• Analyze authors, articles, cited references, core subject categories, and geographic distribution.

Experimental results

Research questions

• RQ1 Who are the most influential and central actors (authors, articles, journals) in the network community detection literature?
• RQ2 What are the major publication venues, countries, and subject areas driving the field?
• RQ3 Which works exhibit notable citation bursts or centrality indicating emerging or enduring impact?
• RQ4 Where does the field originate and how has it geographically and disciplinarily evolved?

Key findings

• Yong Wang is identified as a pivot node with the highest centrality.
• Mark Newman is the most highly cited author in the network.
• The journal Reviews of Modern Physics shows the strongest citation burst.
• Andrea Lancichinetti’s work has the highest centrality among cited documents.
• The origins of key publications primarily lie in the United States, with Scotland exhibiting the strongest and longest citation burst.
• Core categories Cited: Computer Science leads in frequency, while Engineering leads in centrality.