TY - JOUR
T1 - Density-based rough set model for hesitant node clustering in overlapping community detection
AU - Wang, Jun
AU - Peng, Jiaxu
AU - Liu, Ou
N1 - Publisher Copyright:
© 2014 Beijing Institute of Aerospace Information.
PY - 2014/12/1
Y1 - 2014/12/1
N2 - Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years. A notion of hesitant node (HN) is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure. However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model (DBRSM) is proposed by combining the virtue of density-based algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further 'growth' of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.
AB - Overlapping community detection in a network is a challenging issue which attracts lots of attention in recent years. A notion of hesitant node (HN) is proposed. An HN contacts with multiple communities while the communications are not strong or even accidental, thus the HN holds an implicit community structure. However, HNs are not rare in the real world network. It is important to identify them because they can be efficient hubs which form the overlapping portions of communities or simple attached nodes to some communities. Current approaches have difficulties in identifying and clustering HNs. A density-based rough set model (DBRSM) is proposed by combining the virtue of density-based algorithms and rough set models. It incorporates the macro perspective of the community structure of the whole network and the micro perspective of the local information held by HNs, which would facilitate the further 'growth' of HNs in community. We offer a theoretical support for this model from the point of strength of the trust path. The experiments on the real-world and synthetic datasets show the practical significance of analyzing and clustering the HNs based on DBRSM. Besides, the clustering based on DBRSM promotes the modularity optimization.
KW - density-based rough set model (DBRSM)
KW - hesitant node (HN)
KW - overlapping community detection
KW - rough set
KW - trust path
UR - http://www.scopus.com/inward/record.url?scp=84930652855&partnerID=8YFLogxK
U2 - 10.1109/JSEE.2014.00125
DO - 10.1109/JSEE.2014.00125
M3 - Article
AN - SCOPUS:84930652855
SN - 1671-1793
VL - 25
SP - 1089
EP - 1097
JO - Journal of Systems Engineering and Electronics
JF - Journal of Systems Engineering and Electronics
IS - 6
ER -