TY - GEN
T1 - From Densification Power Law to Degree of Separation
T2 - 19th IEEE Intl Conference on High Performance Computing and Communications, 15th IEEE Intl Conference on Smart City, and 3rd IEEE Intl Conference on Data Science and Systems, HPCC/SmartCity/DSS 2017
AU - She, James
AU - Zhao, Chen
AU - Cheung, Ming
AU - Liang, Hao
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The densification power law is a concept in the realm of temporal graph evolution. The number of edges grows in a power law over the number of nodes over time, replacing the pre-2005 general assumption of a linear trend. The densification power law has been verified by several real networks over a long period of time. In this work, one such graph, the arXiv citation network is investigated to examine how the densification power law is working ten years after its publication. The network is evaluated and compared with the discussion in a previous work. It is observed that the graph densification continues over time, but instead of maintaining a constant densification power exponent, as suggested by previous work, the exponent is actually dropping over time, which suggests the densification power law is now fading away. Here, this fading effect is literature analysed, and it is suggested that node capability is the major obstacle to the continuation of the original trend. To fully compare with the previous work on graph evolution, the change of the average path length over time is also investigated on our and other's results. The results imply the decreasing of the average path length in the temporal evolution is very slow, which suggests that there exists a new universal degree of separation in social networks of around three.
AB - The densification power law is a concept in the realm of temporal graph evolution. The number of edges grows in a power law over the number of nodes over time, replacing the pre-2005 general assumption of a linear trend. The densification power law has been verified by several real networks over a long period of time. In this work, one such graph, the arXiv citation network is investigated to examine how the densification power law is working ten years after its publication. The network is evaluated and compared with the discussion in a previous work. It is observed that the graph densification continues over time, but instead of maintaining a constant densification power exponent, as suggested by previous work, the exponent is actually dropping over time, which suggests the densification power law is now fading away. Here, this fading effect is literature analysed, and it is suggested that node capability is the major obstacle to the continuation of the original trend. To fully compare with the previous work on graph evolution, the change of the average path length over time is also investigated on our and other's results. The results imply the decreasing of the average path length in the temporal evolution is very slow, which suggests that there exists a new universal degree of separation in social networks of around three.
UR - http://www.scopus.com/inward/record.url?scp=85047469510&partnerID=8YFLogxK
U2 - 10.1109/HPCC-SmartCity-DSS.2017.36
DO - 10.1109/HPCC-SmartCity-DSS.2017.36
M3 - Conference contribution
AN - SCOPUS:85047469510
T3 - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
SP - 278
EP - 285
BT - Proceedings - 2017 IEEE 19th Intl Conference on High Performance Computing and Communications, HPCC 2017, 2017 IEEE 15th Intl Conference on Smart City, SmartCity 2017 and 2017 IEEE 3rd Intl Conference on Data Science and Systems, DSS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 December 2017 through 20 December 2017
ER -