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Hyperbolic geometry and real life networks |
Dorota Celińska 1, Eryk Kopczyński 2 |
1. University of Warsaw, Faculty of Economic Sciences, 44/50, Długa St., Warsaw 00-241, Poland |
Abstract |
Drawing graphs as nodes connected by links is visually compelling but computationally difficult [1]. In hyperbolic plane, the amount of space in distance d from the given point is exponential in d. Recently it was shown that hyperbolic geometry is intrinsic in many real world networks, and especially useful while modeling large scale-free networks based on similarity and popularity [2]. An efficient embedding algorithm has been recently shown [3]. Unfortunately, this algorithm does not deal with weighted networks, and may not give visually appealing results. Furthermore, no powerful tools exist to visualize such embeddings of graphs. In this article, we present and discuss the goodness of fit of the hyperbolic embeddings of selected networks. RogueViz, a novel tool, based on the Open Source game HyperRogue, is used to map the network and navigate the hyperbolic graph.
We present a visualization of the games discussed in r/roguelikes, constructed upon the posts and comments from Jul 6, 2016 to Mar 11, 2017 which have been downloaded via the Reddit API. For each Reddit user and each game the number of posts/comments where the given user has mentioned the given game is counted. If a user U mentions game A na times and game B nb times, the games A and B attract themselves with force nanb/fU, where fu is chosen so that the total contribution of user U is proportional to the square root of his total number of mentions. The algorithm we use to embed the networks is based on simulated annealing. Our findings suggest that modeling social networks embedded in hyperbolic plane can improve knowledge about the latent communities. Vertices sharing similar properties tend to be mapped close together. The Poincare model enables sort of ``fish-eye'' view at the whole network, comfortable for quick investigation of popularity of nodes. Analyses of log-likelihood reveal that hyperbolic embeddings predict noticeably better the existence of edges in the networks compared to the predictions made by their Euclidean counterparts.
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Presentation: Poster at Econophysics Colloquium 2017, Symposium A, by Dorota CelińskaSee On-line Journal of Econophysics Colloquium 2017 Submitted: 2017-04-11 23:39 Revised: 2017-04-12 08:01 |