An Improved SGN Algorithm Research for Detecting Community Structure in Complex Network

2014 ◽  
pp. 45-53
Author(s):  
Pengfei Du ◽  
Yinghong Ma ◽  
Xiulong Wang
Keyword(s):  
Community Structure ◽  
Complex Network

scholarly journals Benefiting from the community structure in opportunistic forwarding

2013 ◽  
Vol 10 (2) ◽  
pp. 865-876
Author(s):  
Bing Bai ◽  
Zhenqian Feng ◽  
Baokang Zhao ◽  
Jinshu Su
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Single Copy ◽  
Routing Algorithms ◽  
Node Mobility ◽  
Challenging Problem ◽  
Community Based ◽  
Single Node ◽  
Inherent Structure ◽  
Delay Tolerant

In Delay Tolerant Networks (DTNs), an end-to-end connectivity cannot be assumed for node mobility and lack of infrastructure. Due to the uncertainty in nodal mobility, routing in DTNs becomes a challenging problem. To cope with this, many researchers proposed opportunistic routing algorithms based on some utilities. However, these simple metrics may only capture one facet of the single node?s mobility process, which cannot reflect the inherent structure of the networks well. Recently, some researchers introduce the Complex network analysis (CNA) to formulate and predict the future contact in DTNs. The community structure is one of the most important properties of CNA. And it reveals the inherent structure of the complex network. In this paper, we present a community-based single-copy forwarding protocol for DTNs routing, which efficiently utilizes the community structure to improve the forwarding efficiency. Simulation results are presented to support the effectiveness of our scheme.

Book embedding of complex network with community structure

2019 ◽  
Vol 361 ◽  
pp. 747-751
Author(s):  
Bin Zhao ◽  
Wengu Chen ◽  
Jixiang Meng ◽  
Fengxia Liu
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Book Embedding

scholarly journals LIMITED RESOLUTION AND MULTIRESOLUTION METHODS IN COMPLEX NETWORK COMMUNITY DETECTION

2007 ◽  
Vol 07 (03) ◽  
pp. L209-L214 ◽  
Author(s):  
JUSSI M. KUMPULA ◽  
JARI SARAMÄKI ◽  
KIMMO KASKI ◽  
JÁNOS KERTÉSZ
Keyword(s):  
Simulated Annealing ◽  
Community Structure ◽  
Complex Network ◽  
Community Detection ◽  
Real World ◽  
Tuning Parameter ◽  
Challenging Problem ◽  
Resolution Limit ◽  
Network Community ◽  
Multiresolution Methods

Detecting community structure in real-world networks is a challenging problem. Recently, it has been shown that the resolution of methods based on optimizing a modularity measure or a corresponding energy is limited; communities with sizes below some threshold remain unresolved. One possibility to go around this problem is to vary the threshold by using a tuning parameter, and investigate the community structure at variable resolutions. Here, we analyze the resolution limit and multiresolution behavior for two different methods: a q-state Potts method proposed by Reichard and Bornholdt, and a recent multiresolution method by Arenas, Fernández, and Gómez. These methods are studied analytically, and applied to three test networks using simulated annealing.

scholarly journals NONLOCAL ANALYSIS OF MODULAR ROLES

2012 ◽  
Vol 22 (07) ◽  
pp. 1250167
Author(s):  
J. M. BULDÚ ◽  
I. SENDIÑA-NADAL ◽  
I. LEYVA ◽  
J. A. ALMENDRAL ◽  
M. ZANIN ◽  
...  
Keyword(s):  
Community Structure ◽  
Complex Network ◽  
Shortest Paths ◽  
Global Information

We introduce a new methodology to characterize the role that a given node plays inside the community structure of a complex network. Our method relies on the ability of the links to reduce the number of steps between two nodes in the network, which is measured by the number of shortest paths crossing each link, and its impact on the node proximity. In this way, we use node closeness to quantify the importance of a node inside its community. At the same time, we define a participation coefficient that depends on the shortest paths contained in the links that connect two communities. The combination of both parameters allows to identify the role played by the nodes in the network, following the same guidelines introduced by Guimerà et al. [Guimerà & Amaral, 2005] but, in this case, considering global information about the network. Finally, we give some examples of the hub characterization in real networks and compare our results with the parameters most used in the literature.

Active semisupervised community detection based on asymmetric similarity measure

2015 ◽  
Vol 29 (13) ◽  
pp. 1550078 ◽  
Author(s):  
Mingwei Leng ◽  
Liang Huang ◽  
Longjie Li ◽  
Hanhai Zhou ◽  
Jianjun Cheng ◽  
...  
Keyword(s):  
Community Structure ◽  
Prior Knowledge ◽  
Complex Network ◽  
Community Detection ◽  
Detection Algorithm ◽  
Directed Network ◽  
Detection Algorithms ◽  
Community Detection Algorithm ◽  
Similarity Method ◽  
Asymmetric Similarity

Semisupervised community detection algorithms use prior knowledge to improve the performance of discovering the community structure of a complex network. However, getting those prior knowledge is quite expensive and time consuming in many real-world applications. This paper proposes an active semisupervised community detection algorithm based on the similarities between nodes. First, it transforms a given complex network into a weighted directed network based on the proposed asymmetric similarity method, some informative nodes are selected to be the labeled nodes by using an active mechanism. Second, the proposed algorithm discovers the community structure of a complex network by propagating the community labels of labeled nodes to their neighbors based on the similarity between a node and a community. Finally, the performance of the proposed algorithm is evaluated with three real networks and one synthetic network and the experimental results show that the proposed method has a better performance compared with some other community detection algorithms.

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