Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Lorenzo Severini"'
Autor:
Irena Koprinska, Paolo Mignone, Riccardo Guidotti, Szymon Jaroszewicz, Holger Fröning, Francesco Gullo, Pedro M. Ferreira, Damian Roqueiro, Gaia Ceddia, Slawomir Nowaczyk, João Gama, Rita Ribeiro, Ricard Gavaldà, Elio Masciari, Zbigniew Ras, Ettore Ritacco, Francesca Naretto, Andreas Theissler, Przemyslaw Biecek, Wouter Verbeke, Gregor Schiele, Franz Pernkopf, Michaela Blott, Ilaria Bordino, Ivan Luciano Danesi, Giovanni Ponti, Lorenzo Severini, Annalisa Appice, Giuseppina Andresini, Ibéria Medeiros, Guilherme Graça, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Diego Saldana, Konstantinos Sechidis, Arif Canakoglu, Sara Pido, Pietro Pinoli, Albert Bifet, Sepideh Pashami
This volume constitutes the papers of several workshops which were held in conjunction with the International Workshops of ECML PKDD 2022 on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022, held in Gre
Publikováno v:
WI/IAT
Given a set of vertices in a network, that we believe are of interest for the application under analysis, community search is the problem of producing a subgraph potentially explaining the relationships existing among the vertices of interest. In pra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd405768673b62ad04e1d8f8485e66f8
http://arxiv.org/abs/2012.00356
http://arxiv.org/abs/2012.00356
Autor:
Lorenzo Severini, Gianlorenzo D'Angelo, Henning Meyerhenke, Elisabetta Bergamini, Pierluigi Crescenzi, Yllka Velaj
Publikováno v:
ACM Journal of Experimental Algorithmics. 23:1-32
Betweenness is a well-known centrality measure that ranks the nodes according to their participation in the shortest paths of a network. In several scenarios, having a high betweenness can have a positive impact on the node itself. Hence, in this pap
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::426e59c14c091bf62b4b1577429545f6
https://doi.org/10.1145/3166071
https://doi.org/10.1145/3166071
Autor:
Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastian Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic
Autor:
Michael Kamp, Irena Koprinska, Adrien Bibal, Tassadit Bouadi, Benoît Frénay, Luis Galárraga, José Oramas, Linara Adilova, Yamuna Krishnamurthy, Bo Kang, Christine Largeron, Jefrey Lijffijt, Tiphaine Viard, Pascal Welke, Massimiliano Ruocco, Erlend Aune, Claudio Gallicchio, Gregor Schiele, Franz Pernkopf, Michaela Blott, Holger Fröning, Günther Schindler, Riccardo Guidotti, Anna Monreale, Salvatore Rinzivillo, Przemyslaw Biecek, Eirini Ntoutsi, Mykola Pechenizkiy, Bodo Rosenhahn, Christopher Buckley, Daniela Cialfi, Pablo Lanillos, Maxwell Ramstead, Tim Verbelen, Pedro M. Ferreira, Giuseppina Andresini, Donato Malerba, Ibéria Medeiros, Philippe Fournier-Viger, M. Saqib Nawaz, Sebastian Ventura, Meng Sun, Min Zhou, Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini, Rita Ribeiro, João Gama, Ricard Gavaldà, Lee Cooper, Naghmeh Ghazaleh, Jonas Richiardi, Damian Roqueiro, Diego Saldana Miranda, Konstantinos Sechidis, Guilherme Graça
This two-volume set constitutes the refereed proceedings of the workshops which complemented the 21th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in September 2021. Due to the COVID-19 pandemic
Autor:
Valerio Bitetta, Ilaria Bordino, Andrea Ferretti, Francesco Gullo, Giovanni Ponti, Lorenzo Severini
This book constitutes revised selected papers from the 5th Workshop on Mining Data for Financial Applications, MIDAS 2020, held in conjunction with ECML PKDD 2020, in Ghent, Belgium, in September 2020.•The 8 full and 3 short papers presented in thi
Publikováno v:
SIGMOD Conference
The $k$-core of a graph is defined as the maximal subgraph in which every vertex is connected to at least $k$ other vertices within that subgraph. In this work we introduce a distance-based generalization of the notion of $k$-core, which we refer to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1b218a963bef61f13200bd1aeb2340e1
http://arxiv.org/abs/1904.07262
http://arxiv.org/abs/1904.07262
Publikováno v:
Electronic Notes in Theoretical Computer Science. 322:153-168
The betweenness is a well-known measure of centrality of a node in a network. We consider the problem of determining how much a node can increase its betweenness centrality by creating a limited amount of new edges incident to it. If the graph is dir
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34e5e3f400f8f4b10c46909100ce1088
https://doi.org/10.1016/j.entcs.2016.03.011
https://doi.org/10.1016/j.entcs.2016.03.011
The link recommendation problem consists in suggesting a set of links to the users of a social network in order to increase their social circles and the connectivity of the network. Link recommendation is extensively studied in the context of social
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf80ef467ce1b580d210b57ed1d5fc5e
http://arxiv.org/abs/1706.04368
http://arxiv.org/abs/1706.04368
Publikováno v:
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD), 2016, 11 (1), pp.1-32. ⟨10.1145/2953882⟩
ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2016, 11 (1), pp.1-32. ⟨10.1145/2953882⟩
ACM Transactions on Knowledge Discovery from Data (TKDD), 2016, 11 (1), pp.1-32. ⟨10.1145/2953882⟩
ACM Transactions on Knowledge Discovery from Data (TKDD), ACM, 2016, 11 (1), pp.1-32. ⟨10.1145/2953882⟩
The closeness centrality is a well-known measure of importance of a vertex within a given complex network. Having high closeness centrality can have positive impact on the vertex itself: hence, in this paper we consider the optimization problem of de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5fb122985135f2458b231e8aacb1fe5
https://hal.inria.fr/hal-01390134
https://hal.inria.fr/hal-01390134