Community Detection Algorithm Based on the Scale Free Property of Networks

Liu, Xiyang; Pan, Qiong; An, Yingying; Qin, Guimin

doi:10.1109/cit/iucc/dasc/picom.2015.335

Cited by 2 publications

(2 citation statements)

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“…The addition of new nodes does not affect the internal structure of the network, and the connection mechanism of new nodes leads to the scale-free topology being defined as a power-law distribution. The emergence of scalefree structures and power-law degree distributions can be attributed to two mechanisms -growth and preferred attachment [89] -that as new nodes appear, they tend to connect to the nodes which are more linked, and over time, these popular hubs gain more connections than their less connected neighbors. This "getting richer" process often favors early nodes, which are more likely to end up as hubs.…”

Section: Scale-free Networkmentioning

confidence: 99%

“…Community detection [89] Industrial wireless sensor networks [10][11] Delay Tolerant Networks (DTNs) [58] Detection of the community structure of smart city network system [59] Topology planning [88] Grid system [31] East China power grid [32] HUST WU WUT CSU SYSU [29] Network attack [98] Wireless sensor network [92] User-side big data balanced partitioning [94] The end-to-end TCP [91] Job scheduling [60] Joint localization and data acquisition in wireless sensor networks [85][86] Optimize the average network latency of the network [57] Information center network [51] Network routing [12] cally the following, power consumption, delay, ductility, robustness. Figure .7 summarizes the industrial interconnection network topology mentioned above.…”

Section: Small-world Networkmentioning

confidence: 99%

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A Survey on the Network Models applied in the Industrial Network Optimization

Chen¹,

Xiong²,

Xue³

et al. 2022

Preprint

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Network architecture design is very important for the optimization of industrial networks. The type of network architecture can be divided into small-scale network and large-scale network according to its scale. Graph theory is an efficient mathematical tool for network topology modeling. For small-scale networks, its structure often has regular topology. For large-scale networks, the existing research mainly focuses on the random characteristics of network nodes and edges. Recently, popular models include random networks, small-world networks and scale-free networks. Starting from the scale of network, this survey summarizes and analyzes the network modeling methods based on graph theory and the practical application in industrial scenarios. Furthermore, this survey proposes a novel network performance metric -system entropy. From the perspective of mathematical properties, the analysis of its non-negativity, monotonicity and concave-convexity is given. The advantage of system entropy is that it can cover the existing regular network, random network, small-world network and scale-free network, and has strong generality. The simulation results show that this metric can realize the comparison of various industrial networks under different models.

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Section: Scale-free Networkmentioning

confidence: 99%