TY - JOUR
AU - Fournier-Viger, Philippe
AB - For the last decade, social networking websites have boosted interaction among people through the use of digital communication such as chats, comments, discussion boards and exchange of documentation. This lead to mutual learning and sharing of all kind of information. This phenomenon has attracted many researchers and techniques aiming at discovering and prediction links between people have been developed. Most existing solutions are based on the similarity in the profiles using the declared personal information. We present in this paper an approach to discover and predict semantic links between members of a social network based on the content analysis. Our approach uses a textual aggregation function to aggregate keywords extracted from people’s textual production. The result of this aggregation is then used to predict semantic links between the members of the network. Experiments were carried out using a real scientific corpus extracted from ReseachGate Web Site. The obtained results showed that our approach, compared to others, achieves better performances in terms of recall, precision, F-measure, complexity and runtime.
TI - Enhancing link prediction in dynamic networks using content aggregation
JF - Cluster Computing
DO - 10.1007/s10586-021-03290-8
DA - 2021-12-01
UR - https://www.deepdyve.com/lp/springer-journals/enhancing-link-prediction-in-dynamic-networks-using-content-OuSKhXwcHy
SP - 3055
EP - 3063
VL - 24
IS - 4
DP - DeepDyve
ER -