TY - JOUR
T1 - Robustness in network community detection under links weights uncertainties
AU - Ramirez-Marquez, J. E.
AU - Rocco, C. M.
AU - Moronta, J.
AU - Gama Dessavre, D.
N1 - Publisher Copyright:
© 2016 Elsevier Ltd. All rights reserved.
PY - 2016/9/1
Y1 - 2016/9/1
N2 - In network analysis, a community can be defined as a group of nodes of a network (or clusters) that are densely interconnected with each other but only sparsely connected with the rest of the network. Several algorithms have been used to obtain a convenient partition allowing extracting the communities in a given network, based on their topology and, possibly, the weights of links. These weights usually represent specific characteristics for example: distance, reactance, reliability. Even if the optimum partitions could be derived, there are uncertainties associated to the network parameters that affect the network partition. In this paper, the authors extend a previous approach for assessing the effects of weight uncertainties on community structures and propose a global approach for (a) understanding the global similarity among the partitions; (b) analyzing the robustness of the communities derived without uncertainty; and (c) quantifying the robustness of the inter-community links. To this aim an uncertainty propagation analysis, based on the Monte Carlo technique is proposed. The approach is illustrated through analyzing the topology of an electric power system.
AB - In network analysis, a community can be defined as a group of nodes of a network (or clusters) that are densely interconnected with each other but only sparsely connected with the rest of the network. Several algorithms have been used to obtain a convenient partition allowing extracting the communities in a given network, based on their topology and, possibly, the weights of links. These weights usually represent specific characteristics for example: distance, reactance, reliability. Even if the optimum partitions could be derived, there are uncertainties associated to the network parameters that affect the network partition. In this paper, the authors extend a previous approach for assessing the effects of weight uncertainties on community structures and propose a global approach for (a) understanding the global similarity among the partitions; (b) analyzing the robustness of the communities derived without uncertainty; and (c) quantifying the robustness of the inter-community links. To this aim an uncertainty propagation analysis, based on the Monte Carlo technique is proposed. The approach is illustrated through analyzing the topology of an electric power system.
KW - Community detection
KW - networks
KW - robustness
KW - similarity
KW - uncertainty
UR - http://www.scopus.com/inward/record.url?scp=84964584369&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84964584369&partnerID=8YFLogxK
U2 - 10.1016/j.ress.2016.04.009
DO - 10.1016/j.ress.2016.04.009
M3 - Article
AN - SCOPUS:84964584369
SN - 0951-8320
VL - 153
SP - 88
EP - 95
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
ER -