Grounding force-directed network layouts with latent space models

Felix Gaisbauer (), Armin Pournaki, Sven Banisch and Eckehard Olbrich
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Felix Gaisbauer: Weizenbaum Institute for the Networked Society
Armin Pournaki: Max Planck Institute for Mathematics in the Sciences
Sven Banisch: Karlsruhe Institute for Technology
Eckehard Olbrich: Max Planck Institute for Mathematics in the Sciences

Journal of Computational Social Science, 2023, vol. 6, issue 2, No 10, 707-739

Abstract: Abstract Force-directed layout algorithms are ubiquitously used tools for network visualization. However, existing algorithms either lack clear interpretation, or they are based on techniques of dimensionality reduction which simply seek to preserve network-immanent topological features, such as geodesic distance. We propose an alternative layout algorithm. The forces of the algorithm are derived from latent space models, which assume that the probability of nodes forming a tie depends on their distance in an unobserved latent space. As opposed to previous approaches, this grounds the algorithm in a plausible interaction mechanism. The forces infer positions which maximise the likelihood of the given network under the latent space model. We implement these forces for unweighted, multi-tie, and weighted networks. We then showcase the algorithm by applying it to Facebook friendship, and Twitter follower and retweet networks; we also explore the possibility of visualizing data traditionally not seen as network data, such as survey data. Comparison to existing layout algorithms reveals that node groups are placed in similar configurations, while said algorithms show a stronger intra-cluster separation of nodes, as well as a tendency to separate clusters more strongly in multi-tie networks, such as Twitter retweet networks.

Keywords: Network embeddings; Force-directed layout algorithms; Latent space models; Network visualization; Social networks (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s42001-023-00207-w

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