How to Create and Visualize Scopus Co-Author Networks in VOSviewer || Research Network || Hindi 2024

Scopus co-author networks can be built and visualized in VOSviewer by exporting bibliographic data from Google Scholar (or other sources) and then mapping co-authorship relationships into an interpretable graph. The practical payoff is immediate: researchers can see which co-authors cluster together, how dense collaboration is across an author set, and which names sit at the center of a collaboration network—useful for understanding productivity patterns and for sharing results on social media or in reports.

The workflow starts with installing VOSviewer on a Windows system and ensuring Java is available, since VOSviewer depends on it. After installation, the next step is data collection. The transcript walks through pulling publication records from Google Scholar: select relevant items, export citation data (including citation information), and download the resulting file to a local folder. Because VOSviewer can’t read every export format, the author filters the input by choosing the appropriate bibliographic data source during the mapping step.

In VOSviewer, the process then moves to creating a map based on bibliographic data. The key configuration is selecting the data source (the transcript notes that Web of Science formats may not be supported in the same way, so Google Scholar exports are used), then setting thresholds that control the network size. Examples include ignoring documents with very large author counts (to avoid noisy, overly broad collaborations), setting a minimum number of documents per author, and selecting the author field to include. Once these parameters are set, VOSviewer generates a co-authorship network graph.

Three visualizations are emphasized as decision tools: the network view (showing nodes and links between authors), the overlay view (allowing inspection of connections by clicking nodes/edges), and the density view (highlighting where collaboration is strongest). The density layer is particularly important for identifying which co-authors have higher collaboration frequency—areas with stronger density indicate more frequent co-publication relationships.

The transcript also covers practical output and sharing. The generated visualization can be exported as images (such as PNG), with options to adjust formatting and save the graph for presentations or social media. For users who need more than a limited free lookup, the transcript recommends signing in to access a larger number of publications and then exporting the selected document set.

Finally, the guide notes that the same approach can be repeated for other bibliographic sources and other research profiles, and that VOSviewer offers additional controls (like scaling/zooming and switching application views) to refine how the network is presented. The overall message is that once the data export and VOSviewer mapping settings are understood, co-author network analysis becomes a repeatable method for tracking collaboration patterns and communicating research relationships clearly to others.