A genetic algorithm for spatiosocial tensor clustering
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ORIGINAL PAPER
A genetic algorithm for spatiosocial tensor clustering Exploiting tensorflow potential Georgios Drakopoulos1,2 · Foteini Stathopoulou3 · Andreas Kanavos4 · Michael Paraskevas5 · Giannis Tzimas5 · Phivos Mylonas2 · Lazaros Iliadis6 Received: 22 May 2018 / Accepted: 19 December 2018 © Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract Tensor clustering is a knowledge management technique which is well known as a major algorithmic and technological driver behind a broad applications spectrum. The latter ranges from multimodal social media analysis and geolocation processing to analytics tailored for large omic data. However, known exact tensor clustering problems when reduced to tensor factorization are provably NP hard. This is attributed in part to the volume of data contained in a tensor, proportional to the product of its dimensions, as well as to the increased interdependency between the tensor entries across its dimensions. One well studied way to circumvent this inherent difficulty is to resort to heuristics. This article presents an enhanced version of a genetic algorithm tailored for community discovery structure in tensors containing spatiosocial data, namely linguistic and geolocation data. The objective function as well as the chromosome fitness functions by design take into account elements of linguistic propagation models. The genetic operators of selection, crossover, and mutation as well as the newly added double mutation operator work directly on the community level. Moreover, various policies for maintaining gene variability across generations are studied in an extensive simulation powered by Google TensorFlow. As with its predecessor, the proposed genetic algorithm has been applied to a dataset consisting of a large number of Tweets and their associated geolocations from the Grand Duchy of Luxembourg, a historically and de facto trilingual country. The results are compared with those obtained from the original genetic algorithm and their differences are interpreted. Keywords Multilingual social networks · Multimodal social networks · Cross cultural communication · Language variation models · Tensor clustering · Google TensorFlow · Genetic algorithms · Gene variability · Geolocation data · Spatiosocial data · Humanistic data · Higher order data Mathematics Subject Classification 05C76 · 05C85 · 05D99 · 62H30 · 91C20 · 91C99 CR Subject Classification H.2.8 · G.2.2 · G.3 · M.1
* Georgios Drakopoulos [email protected]
Lazaros Iliadis [email protected]
Foteini Stathopoulou [email protected]
1
Cloudminers Inc., Corfu, Greece
Andreas Kanavos [email protected]
2
Department of Informatics, Ionion University, Corfu, Greece
3
University of Luxembourg, Luxembourg City, Luxembourg
4
Hellenic Open University, Patras, Greece
5
Technological and Educational Institution of Western Greece, Patras, Greece
6
Forest Informatics Lab, Democritus University of Thrace, Komotini, Greece
Michael Paraskevas [email protected] Giannis Tzimas tzim
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