%0 Journal Article %J arXiv preprint arXiv:1905.01498 %D 2019 %T DynComm R Package–Dynamic Community Detection for Evolving Networks %A Sarmento, Rui Portocarrero %A Lemos, Luís %A Cordeiro, Mário %A Giulio Rossetti %A Cardoso, Douglas %B arXiv preprint arXiv:1905.01498 %G eng %0 Conference Paper %B International Conference on Complex Networks CompleNet %D 2018 %T Diffusive Phenomena in Dynamic Networks: a data-driven study %A Letizia Milli %A Giulio Rossetti %A Dino Pedreschi %A Fosca Giannotti %X Everyday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work – following a data-driven approach – we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models. %B International Conference on Complex Networks CompleNet %I Springer %C Boston March 5-8 2018 %G eng %U https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13 %R 10.1007/978-3-319-73198-8_13 %0 Conference Paper %B International Workshop on Complex Networks %D 2018 %T Discovering Mobility Functional Areas: A Mobility Data Analysis Approach %A Lorenzo Gabrielli %A Daniele Fadda %A Giulio Rossetti %A Mirco Nanni %A Piccinini, Leonardo %A Dino Pedreschi %A Fosca Giannotti %A Patrizia Lattarulo %X How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained. %B International Workshop on Complex Networks %I Springer %G eng %U https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27 %R 10.1007/978-3-319-73198-8_27 %0 Conference Paper %B European Conference on Parallel Processing %D 2017 %T Dynamic community analysis in decentralized online social networks %A Guidi, Barbara %A Michienzi, Andrea %A Giulio Rossetti %B European Conference on Parallel Processing %I Springer %G eng %0 Conference Paper %B KDD 2012 %D 2012 %T DEMON: a Local-First Discovery Method for Overlapping Communities %A Michele Coscia %A Giulio Rossetti %A Fosca Giannotti %A Dino Pedreschi %B KDD 2012 %8 2012 %0 Conference Paper %B The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} '12, Beijing, China, August 12-16, 2012 %D 2012 %T DEMON: a local-first discovery method for overlapping communities %A Michele Coscia %A Giulio Rossetti %A Fosca Giannotti %A Dino Pedreschi %B The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} '12, Beijing, China, August 12-16, 2012 %G eng %U http://doi.acm.org/10.1145/2339530.2339630 %R 10.1145/2339530.2339630