Learning pretopological spaces to extract ego-centered communities
| Autor: | Guillaume Cleuziou, Gaëtan Caillaut, Nicolas Dugué |
|---|---|
| Přispěvatelé: | Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO), Ecole Nationale Supérieure d'Ingénieurs de Bourges-Université d'Orléans (UO), Laboratoire d'Informatique de l'Université du Mans (LIUM), Le Mans Université (UM) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
media_common.quotation_subject Closure (topology) 02 engineering and technology Machine learning computer.software_genre [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI] [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 020204 information systems Id ego and super-ego 0202 electrical engineering electronic engineering information engineering Feature (machine learning) Quality (business) ComputingMilieux_MISCELLANEOUS media_common Ground truth business.industry Node (networking) Logical combination [INFO.INFO-WB]Computer Science [cs]/Web Pretopological space 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), Apr 2019, Macau, China Advances in Knowledge Discovery and Data Mining ISBN: 9783030161446 PAKDD (2) |
| Popis: | We present a pretopological based approach to extract ego-centered communities. Classical methods often consider only one structural feature of the network, whereas pretopology enables to do multi-criteria analysis. Our approach consists in learning a logical combination of network’s descriptors to define a pretopological space. Ego-centered communities are extracted by computing the elementary closure of each node. The quality of such communities is evaluated against the ground truth communities. We show the benefits of our method by comparing it to others on both real and synthetic networks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|