Motivated by social balance theory, we develop a theory of link classification in signed networks using the correlation clustering index as measure of label regularity. We derive learning bounds in terms of correlation clustering within three fundamental transductive learning settings: online, batch and active. Our main algorithmic contribution is in the active setting, where we introduce a new family of efficient link classifiers based on covering the input graph with small circuits. These are the first active algorithms for link classification with mistake bounds that hold for arbitrary signed networks.
A correlation clustering approach to link classification in signed networks / N. Cesa-Bianchi, C. Gentile, F. Vitale, G. Zappella - In: Proceedings of the 25th annual Conference on Learning Theory : june 25-27, 2012, Edinburgh, Scotland / [a cura di] S. Mannor, N. Srebro, R.C. Williamson. - Brookline, USA : Microtome, 2012. - pp. 34.1-34.20 (( Intervento presentato al 25. convegno Annual Conference on Learning Theory tenutosi a Edinburgh nel 2012.
|Titolo:||A correlation clustering approach to link classification in signed networks|
CESA BIANCHI, NICOLO' ANTONIO (Primo)
VITALE, FABIO (Penultimo)
ZAPPELLA, GIOVANNI (Ultimo)
|Parole Chiave:||Active learning; Online learning; Social balance theory; Transductive learning|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Progetto:||Pattern Analysis, Statistical Modelling and Computational Learning 2|
|Data di pubblicazione:||2012|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|