The Structure and Dynamics of Co-Citation Clusters: A Multiple-Perspective Co-Citation Analysis

TL;DR

This paper introduces a multi-perspective co-citation analysis method that combines visualization, clustering, labeling, and summarization to better understand the structure and evolution of co-citation networks in scientific fields.

Contribution

The paper presents a novel integrated approach for analyzing co-citation clusters, enhancing interpretability and insight into scientific literature dynamics.

Findings

Improved interpretability of co-citation networks.

Effective automatic cluster labeling and summarization.

Insights into the evolution of information science over time.

Abstract

A multiple-perspective co-citation analysis method is introduced for characterizing and interpreting the structure and dynamics of co-citation clusters. The method facilitates analytic and sense making tasks by integrating network visualization, spectral clustering, automatic cluster labeling, and text summarization. Co-citation networks are decomposed into co-citation clusters. The interpretation of these clusters is augmented by automatic cluster labeling and summarization. The method focuses on the interrelations between a co-citation cluster's members and their citers. The generic method is applied to a three-part analysis of the field of Information Science as defined by 12 journals published between 1996 and 2008: 1) a comparative author co-citation analysis (ACA), 2) a progressive ACA of a time series of co-citation networks, and 3) a progressive document co-citation analysis (DCA). Results show that the multiple-perspective method increases the interpretability and accountability of both ACA and DCA networks.