TY - JOUR
T1 - Structural properties and generative model of non-giant connected components in social networks
AU - Niu, Jianwei
AU - Wang, Lei
N1 - Publisher Copyright:
© 2016, Science China Press and Springer-Verlag Berlin Heidelberg.
PY - 2016/12/1
Y1 - 2016/12/1
N2 - Most previous studies have mainly focused on the analyses of one entire network (graph) or the giant connected components of networks. In this paper, we investigate the disconnected components (non-giant connected component) of some real social networks, and report some interesting discoveries about structural properties of disconnected components. We study three diverse, real networks and compute the significance profile of each component. We discover some similarities in the local structure between the giant connected component and disconnected components in diverse social networks. Then we discuss how to detect network attacks based on the local structure properties of networks. Furthermore, we propose an empirical generative model called iFriends to generate networks that follow our observed patterns.
AB - Most previous studies have mainly focused on the analyses of one entire network (graph) or the giant connected components of networks. In this paper, we investigate the disconnected components (non-giant connected component) of some real social networks, and report some interesting discoveries about structural properties of disconnected components. We study three diverse, real networks and compute the significance profile of each component. We discover some similarities in the local structure between the giant connected component and disconnected components in diverse social networks. Then we discuss how to detect network attacks based on the local structure properties of networks. Furthermore, we propose an empirical generative model called iFriends to generate networks that follow our observed patterns.
KW - disconnected components
KW - generative model
KW - giant connected component
KW - significance profile
KW - structural properties
UR - https://www.scopus.com/pages/publications/84994852265
U2 - 10.1007/s11432-015-0790-x
DO - 10.1007/s11432-015-0790-x
M3 - 文章
AN - SCOPUS:84994852265
SN - 1674-733X
VL - 59
JO - Science China Information Sciences
JF - Science China Information Sciences
IS - 12
M1 - 123101
ER -