Abstract
Gephi is a network visualization software used in various disciplines (social network analysis, biology, genomics…). One of its key features is the ability to display the spatialization process, aiming at transforming the network into a map, and ForceAtlas2 is its default layout algorithm. The latter is developed by the Gephi team as an all-around solution to Gephi users’ typical networks (scale-free, 10 to 10,000 nodes). We present here for the first time its functioning and settings. ForceAtlas2 is a force-directed layout close to other algorithms used for network spatialization. We do not claim a theoretical advance but an attempt to integrate different techniques such as the Barnes Hut simulation, degree-dependent repulsive force, and local and global adaptive temperatures. It is designed for the Gephi user experience (it is a continuous algorithm), and we explain which constraints it implies. The algorithm benefits from much feedback and is developed in order to provide many possibilities through its settings. We lay out its complete functioning for the users who need a precise understanding of its behaviour, from the formulas to graphic illustration of the result. We propose a benchmark for our compromise between performance and quality. We also explain why we integrated its various features and discuss our design choices.
Highlights
To Gephi users, we offer a complete description of the ForceAtlas2 algorithm and its settings
If developing an algorithm is ‘‘research’’ and implementing it is ‘‘engineering’’, a specificity of Gephi overall, is that it is based in engineering rather than in research. This is why it looks so different to a software like Pajek. This is why ForceAtlas2 is more about usability than originality
We developed ForceAtlas2 by combining existing techniques
Summary
Yifan Hu [7] uses this technique at the end, during a refining phase He remarks that ‘‘for an application of a force-directed algorithm from a random initial layout, an adaptive step length update scheme is more successful in escaping from local minimums. A medium value of 0.01 has a good convergence and a good final quality, but the adaptive local speed achieves even better on convergence as well as on final quality Yifan Hu has a better performance on small networks while ForceAtlas has a better measured quality, though evaluating the readability of different layouts would require a different discussion and protocol. The LinLog mode of ForceAtlas brings more quality at the price of performance, and Fruchterman-Reingold performs poorly on large networks
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