TY - GEN
T1 - Exploring the Effects of Link Recommendations on Social Networks
T2 - 2020 Spring Simulation Conference, SpringSim 2020
AU - Sibley, Ciara
AU - Crooks, Andrew T.
N1 - Publisher Copyright: © 2020 SCS.
PY - 2020/5
Y1 - 2020/5
N2 - The vast majority of recommender system research has focused on improving performance accuracy, while limited work has explored their societal, network level effects. This paper demonstrates how simulation can be used to investigate macro level effects of online social network link recommendations, such as whether these technologies may be fragmenting or bridging communities of individuals. An agent-based model is presented that generates stylized online social networks with different percentages of real world contacts and link recommendations. Results show that networks with higher percentages of recommendation-based links produce more clustered, distinct, and dispersed communities, suggesting that these technologies could fragment society. Furthermore, scale-free network properties diminished with higher percentages of recommendations, suggesting that these technologies could be contributing to recent findings that social networks are at most 'weakly' scale-free. Building upon this research, further simulation work could inform the design of link recommendation algorithms that help connect both individuals and communities.
AB - The vast majority of recommender system research has focused on improving performance accuracy, while limited work has explored their societal, network level effects. This paper demonstrates how simulation can be used to investigate macro level effects of online social network link recommendations, such as whether these technologies may be fragmenting or bridging communities of individuals. An agent-based model is presented that generates stylized online social networks with different percentages of real world contacts and link recommendations. Results show that networks with higher percentages of recommendation-based links produce more clustered, distinct, and dispersed communities, suggesting that these technologies could fragment society. Furthermore, scale-free network properties diminished with higher percentages of recommendations, suggesting that these technologies could be contributing to recent findings that social networks are at most 'weakly' scale-free. Building upon this research, further simulation work could inform the design of link recommendation algorithms that help connect both individuals and communities.
KW - agent-based modeling
KW - friend-of-friend recommender
KW - mutual connection link recommendation system
KW - online social network
KW - social network analysis
UR - https://www.scopus.com/pages/publications/85092037683
U2 - 10.22360/SpringSim.2020.HSAA.006
DO - 10.22360/SpringSim.2020.HSAA.006
M3 - Conference contribution
T3 - Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020
BT - Proceedings of the 2020 Spring Simulation Conference, SpringSim 2020
A2 - Barros, Fernando J.
A2 - Hu, Xiaolin
A2 - Kavak, Hamdi
A2 - Del Barrio, Alberto A.
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 May 2020 through 21 May 2020
ER -