title: Optimal learning paths in information networks.
abstract: Each sphere of knowledge and information could be depicted as a complex mesh of correlated items. By properly exploiting these connections, innovative and more efficient navigation strategies could be defined, possibly leading to a faster learning process and an enduring retention of information. In this work we investigate how the topological structure embedding the items to be learned can affect the efficiency of the learning dynamics. To this end we introduce a general class of algorithms that simulate the exploration of knowledge/information networks standing on well-established findings on educational scheduling, namely the spacing and lag effects. While constructing their learning schedules, individuals move along connections, periodically revisiting some concepts, and sometimes jumping on very distant ones. In order to investigate the effect of networked information structures on the proposed learning dynamics we focused both on synthetic and real-world graphs such as subsections of Wikipedia and word-association graphs. We highlight the existence of optimal topological structures for the simulated learning dynamics whose efficiency is affected by the balance between hubs and the least connected items. Interestingly, the real-world graphs we considered lead naturally to almost optimal learning performances.
authors.affiliation: 1] Polytechnic University of Turin, Dept. of Mathematical Sciences, Corso Duca degli Abruzzi, 24, 10129 Turin, Italy [2] Institute for Scientific Interchange (ISI), Via Alassio 11C, 10126 Turin, Italy.
authors.forename: G C
authors.initials: GC
authors.lastname: Rodi
authors.affiliation: 1] Sapienza University of Rome, Physics Dept., Piazzale Aldo Moro 2, 00185 Rome, Italy [2] Institute for Scientific Interchange (ISI), Via Alassio 11C, 10126 Turin, Italy [3] SONY-Computer Science Lab (CSL), 5, Rue Amyot, 75005, Paris, France.
authors.forename: V
authors.initials: V
authors.lastname: Loreto
authors.affiliation: 1] Institute for Complex Systems (ISC-CNR), Via dei Taurini 19, 00185 Rome, Italy [2] Sapienza University of Rome, Physics Dept., Piazzale Aldo Moro 2, 00185 Rome, Italy.
authors.forename: V D P
authors.initials: VD
authors.lastname: Servedio
authors.affiliation: Institute for Scientific Interchange (ISI), Via Alassio 11C, 10126 Turin, Italy.
authors.forename: F
authors.initials: F
authors.lastname: Tria
mesh_terms: D003195:Computer Communication Networks; D006801:Humans; D007255:Information Services; D007858:Learning; D008962:Models, Theoretical