Erschienen:
Association for the Advancement of Artificial Intelligence (AAAI), 2021
Erschienen in:
Proceedings of the International AAAI Conference on Web and Social Media, 6 (2021) 1, Seite 443-446
Sprache:
Nicht zu entscheiden
DOI:
10.1609/icwsm.v6i1.14337
ISSN:
2162-3449;
2334-0770
Entstehung:
Anmerkungen:
Beschreibung:
<jats:p>
Traditionally, social network analyses are applied to data from a particular social domain. With the advent of online social networks such as Facebook, we observe an aggregate of various social domains resulting in a layered mix of professional contacts, family ties, and different circles. These aggregates dilute the community structure. We provide a method for de-layering social networks according to shared interests. Instead of relying on changes in the edge density, our shared taste model uses content of users to disambiguate the underlying shared interest of each friendship. We successfully de-layer real world networks from LibraryThing and Boards.ie, obtaining topics that significantly outperform LDA on unsupervised prediction of group membership.
</jats:p>