Pregled bibliografske jedinice broj: 557224
Reasoning about Large-Scale Social Networks with Probabilistic Logic
Reasoning about Large-Scale Social Networks with Probabilistic Logic // Proceedings from Sunbelt XXXI. Trade Winds Beach Resort / H. Russell Bernard, Mark House, Christopher McCarty, John Skvoretz (ur.).
St. Pete Beach (FL): International Network for Social Network Analysis, 2011. str. 137-138 (predavanje, međunarodna recenzija, sažetak, ostalo)
CROSBI ID: 557224 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Reasoning about Large-Scale Social Networks with Probabilistic Logic
Autori
Lauc, Davor ; Grgić, Siniša
Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, ostalo
Izvornik
Proceedings from Sunbelt XXXI. Trade Winds Beach Resort
/ H. Russell Bernard, Mark House, Christopher McCarty, John Skvoretz - St. Pete Beach (FL) : International Network for Social Network Analysis, 2011, 137-138
ISBN
88-901826-5-2
Skup
International Network for Social Network Analysis, XXXI Sunbelt Conference
Mjesto i datum
Saint Pete Beach (FL), Sjedinjene Američke Države, 08.02.2011. - 13.02.2011
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Probabilistic Logic; Social Networks Analysis
Sažetak
Network Matching and Link Prediction are relatively unexplored in the area of Social network analysis, but solving those problems in an efficient way is crucial in many real‐world applications. Network Matching is a generalized problem of node identification. Node identification (matching individuals) is a task of unique identification a person in the analyzed network as a known entity in existing network, based on the known links and additional attributes. In the Link Prediction (social) graph is build or completed by inferencing links based on existing network's structure and node attributes. Both problems in the most realworld applications have to deal with incomplete information and probabilities. In perfect information environment, those problems would be naturally modelled in the predicate logic, hence, the real‐ world problems require methods of probabilistic logic ("ProbLog" framework). Two large‐scale social networks were used to develop and test the model: (1) the sample of the largest social network consisting of 372 volunteers with over 1M links ; (2) huge social network generated from all available Croatian public records with 540.000 individuals and over 100 million links among them. First network was matched with the second using developed model, with completeness of 86, 8% (323 individuals). Results are evaluated against matched volunteers with an error of 5, 3%. Probabalistic logic link prediction model was applied on a second network with promising results.
Izvorni jezik
Engleski
Znanstvena područja
Filozofija
POVEZANOST RADA
Projekti:
130-1203164-0741 - Logika, univerzalni jezik i filozofija jezika (Švob, Goran, MZOS ) ( CroRIS)
Ustanove:
Filozofski fakultet, Zagreb