TY - GEN
T1 - Measuring centrality metrics based on time-ordered graph in mobile social networks
AU - Zhou, Huan
AU - Zhu, Chunsheng
AU - Leung, Victor C.M.
AU - Xu, Shouzhi
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - One important issue in the study of Mobile Social Networks (MSNs) is to measure the centrality (importance) of nodes in networks. However, when measuring the centrality metrics in a certain time interval, the current studies in MSNs focus on analyzing static aggregation networks that do not change over time. Actually, network topology in MSNs is changing very rapidly, which is driven by natural social behavior of people. Therefore, it will not be accurate if the static aggregation network graph is used to measure centrality metrics in a period of time. In this paper, to solve this problem, we first introduce a time-ordered aggregation model, which reduces a dynamic network to a series of time-ordered networks. Then, we propose three particular time-ordered aggregation methods to measure the centrality of nodes in a certain period under two widely used centrality metrics, namely Betweenness centrality and Degree centrality. Finally, extensive trace-driven simulations are conducted to evaluate the performance of different aggregation methods. The results show that the time-ordered aggregation methods can measure the Betweenness and Degree centrality in a time interval more accurately than the Static Aggregation Method, and the Exponential Time-ordered Aggregation Method performs much better than other aggregation methods. Therefore, we recommend to use the Exponential Time-ordered Aggregation Method to measure centrality metrics in a certain time interval.
AB - One important issue in the study of Mobile Social Networks (MSNs) is to measure the centrality (importance) of nodes in networks. However, when measuring the centrality metrics in a certain time interval, the current studies in MSNs focus on analyzing static aggregation networks that do not change over time. Actually, network topology in MSNs is changing very rapidly, which is driven by natural social behavior of people. Therefore, it will not be accurate if the static aggregation network graph is used to measure centrality metrics in a period of time. In this paper, to solve this problem, we first introduce a time-ordered aggregation model, which reduces a dynamic network to a series of time-ordered networks. Then, we propose three particular time-ordered aggregation methods to measure the centrality of nodes in a certain period under two widely used centrality metrics, namely Betweenness centrality and Degree centrality. Finally, extensive trace-driven simulations are conducted to evaluate the performance of different aggregation methods. The results show that the time-ordered aggregation methods can measure the Betweenness and Degree centrality in a time interval more accurately than the Static Aggregation Method, and the Exponential Time-ordered Aggregation Method performs much better than other aggregation methods. Therefore, we recommend to use the Exponential Time-ordered Aggregation Method to measure centrality metrics in a certain time interval.
UR - http://www.scopus.com/inward/record.url?scp=85045257738&partnerID=8YFLogxK
U2 - 10.1109/VTCFall.2017.8288148
DO - 10.1109/VTCFall.2017.8288148
M3 - 会议稿件
AN - SCOPUS:85045257738
T3 - IEEE Vehicular Technology Conference
SP - 1
EP - 5
BT - 2017 IEEE 86th Vehicular Technology Conference, VTC Fall 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 86th IEEE Vehicular Technology Conference, VTC Fall 2017
Y2 - 24 September 2017 through 27 September 2017
ER -