TY  - JOUR
PB  - Public Library of Science
SN  - 1932-6203
A1  - Liao, Hao
A1  - Xiao, Rui
A1  - Cimini, Giulio
A1  - Medo, Matú?
N2  - The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher <italic>h</italic>-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.
KW  - Econolhysics
KW  -  Algorithms
KW  -  Social networks
KW  -  Aging
KW  -  Social research
Y1  - 2014///
AV  - public
TI  - Network-Driven Reputation in Online Scientific Communities
JF  - PloS One
IS  - 12
UR  - http://dx.doi.org/10.1371%2Fjournal.pone.0112022
VL  - 9
ID  - eprints2849
ER  -