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Identifying and characterizing key nodes among communities based on electrical-circuit networks.

Zhu F, Wang W, Di Z, Fan Y - PLoS ONE (2014)

Bottom Line: Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure.Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities.Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

View Article: PubMed Central - PubMed

Affiliation: School of Systems Science, Beijing Normal University, Beijing, China.

ABSTRACT
Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

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The usage of our method in the directed SFI scientist collaboration network.(a) Schematic of the SFI scientist collaboration network. Node diameters indicate the C index value, the color of each node is proportional to the index D. (b,c) The current-flow centrality C and index D for the directed SFI scientist collaboration network.
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pone-0097021-g008: The usage of our method in the directed SFI scientist collaboration network.(a) Schematic of the SFI scientist collaboration network. Node diameters indicate the C index value, the color of each node is proportional to the index D. (b,c) The current-flow centrality C and index D for the directed SFI scientist collaboration network.

Mentions: Applying the directed electrical-circuit network paradigm, we investigate the SFI scientific collaboration network. We convert it to be a directed network by randomly a direction to each of the edges. The result shown in Fig. 8(c) indicates that node 72, 87, 106, and 2 have high values of , all these nodes act as connection points among communities. Due to the fact that node 106 has a high value of and a large value of the , it can be considered to be a bridging node. In fact, from visual inspection of Fig. 8(a), we find that it has primarily inward-directed edges and only a few edges directed toward other communities, which means that this node transfers information that is received from the outside and spread in communities. Nodes 72 and 87 have similar characteristics, while node 2 behaves more like an overlapping node.


Identifying and characterizing key nodes among communities based on electrical-circuit networks.

Zhu F, Wang W, Di Z, Fan Y - PLoS ONE (2014)

The usage of our method in the directed SFI scientist collaboration network.(a) Schematic of the SFI scientist collaboration network. Node diameters indicate the C index value, the color of each node is proportional to the index D. (b,c) The current-flow centrality C and index D for the directed SFI scientist collaboration network.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4045573&req=5

pone-0097021-g008: The usage of our method in the directed SFI scientist collaboration network.(a) Schematic of the SFI scientist collaboration network. Node diameters indicate the C index value, the color of each node is proportional to the index D. (b,c) The current-flow centrality C and index D for the directed SFI scientist collaboration network.
Mentions: Applying the directed electrical-circuit network paradigm, we investigate the SFI scientific collaboration network. We convert it to be a directed network by randomly a direction to each of the edges. The result shown in Fig. 8(c) indicates that node 72, 87, 106, and 2 have high values of , all these nodes act as connection points among communities. Due to the fact that node 106 has a high value of and a large value of the , it can be considered to be a bridging node. In fact, from visual inspection of Fig. 8(a), we find that it has primarily inward-directed edges and only a few edges directed toward other communities, which means that this node transfers information that is received from the outside and spread in communities. Nodes 72 and 87 have similar characteristics, while node 2 behaves more like an overlapping node.

Bottom Line: Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure.Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities.Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

View Article: PubMed Central - PubMed

Affiliation: School of Systems Science, Beijing Normal University, Beijing, China.

ABSTRACT
Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

Show MeSH