Optimizing Online Social Networks for Information Propagation

• DOI: 10.1371/journal.pone.0096614
• Publication Date: 2014-05-09T21:15:12Z
• Authors: Duan-Bing Chen; Guan-Nan Wang; An Zeng; Yan Fu; Yi-Cheng Zhang
• Keywords: Information Overload; Similarity (geometry); Similarity measure; Empirical Research; Social network (sociolinguistics)

Online users nowadays are facing serious information overload problem. In recent years, recommender systems have been widely studied to help people find relevant information. Adaptive social recommendation is one of these in which the connections online networks optimized for propagation so that can receive interesting news or stories from their leaders. Validation such adaptive methods literature assumes uniform distribution users' activity frequency. this paper, our empirical analysis shows actually heterogenous. Accordingly, we propose a more realistic multi-agent model frequency drawn power-law distribution. We previous lead delay since many connected inactive To solve problem, design new similarity measure takes into account frequencies. With measure, average significantly shortened and accuracy largely improved.