INTRODUCTION

One of the initial attempts to map networks in social structures was made by a German primary school teacher Johannes Delitsch. He built a friendship network upon the students of his class from 1880 to 1881. The network delineates reciprocity among students, and considers the influence of class ranking order on their relationships. I acquired the dataset of this study and visualized it on Gephi. 

MATERIALS

I found the dataset on Github.

I used Gephi, an open source network analysis and visualization software to prepare the following visualizations.

PROCESS

I imported the file onto Gephi and ran the statistics. It helped in analyzing the social network. Hereafter I began playing with the appearance and layouts to see what depicted the network dynamics best. I used ForceAtlas 2 for the layout, and I applied a color palette to the nodes and edges. For the following visualization, I used a range of 10 – 30 for the size of the nodes. In this, the attribute depicting the class ranking of the students determined the size. I realized that the highest ranking student is represented within a small circle, and by a light color which made it even less appreciable.

I improvised in this next visualization by inverting the size range to represent the higher ranking students with larger nodes. I did not invert the color scheme to avoid complete non-recognition of the smaller circles. I colored the edges based on weight. In the preview section, I gave labels to the nodes and deselected curved lines for edges. This is a directional network which illustrates reciprocity via the arrow heads.

RESULTS

It appears through the visualization that most high ranking students are also the most popular ones in class, as they are condensed in the center with several bi-directional arrows suggesting reciprocity. All the students except a few isolated ones are well connected within this friendship network. Predominantly, the friendships are also reciprocated.

REFLECTION

Gephi worked well in representing this social network with analytical insights. I liked the feature of statistics which offers an instant analytical scan before designing the visualization. However, since Gephi is a very heavy software, it kept crashing constantly and that hindered my design process.

I would further like to explore this software with different forms of data, and networks with higher number of nodes.