Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks

Cao, Jie; Gao, Guangliang; Tao, Haicheng

doi:10.1016/j.physa.2016.06.113

Cited by 55 publications

(25 citation statements)

References 21 publications

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“…Benefited from the underlying implications, many community-detection methods have been proposed and developed during the past decade [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]. Typical examples include the spectral analysis [22], random walk [23][24][25], label propagation [30], dynamic evolutionary [26][27][28][29], and modularity optimization [31,32].…”

Section: Introductionmentioning

confidence: 99%

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

et al. 2020

PLoS ONE

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Community detection in complex networks is an important issue in network science. Several statistical measures have been proposed and widely applied to detecting the communities in various complex networks. However, due to the lack of flexibility resolution, some of them have to encounter the resolution limit and thus are not compatible with multi-scale structures of complex networks. In this paper, we investigated a statistical measure of interest for community detection, Significance [Sci. Rep. 3 (2013) 2930], and analyzed its critical behaviors based on the theoretical derivation of critical number of communities and the phase diagram in community-partition transition. It was revealed that Significance exhibits far higher resolution than the traditional Modularity when the intra-and inter-link densities of communities are obviously different. Following the critical analysis, we developed a multi-resolution version of Significance for identifying communities in the multi-scale networks. Experimental tests in several typical networks have been performed and confirmed that the generalized Significance can be competent for the multi-scale communities detection. Moreover, it can effectively relax the first-and second-type resolution limits. Finally, we displayed an important potential application of the multi-scale Significance in computational biology: diseasegene identification, showing that extracting information from the perspective of multi-scale module mining is helpful for disease gene prediction.

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Section: Introductionmentioning

confidence: 99%

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

et al. 2020

PLoS ONE

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“…Future research topics would include the extension of the main results obtained in this article to more complex systems with more complicated network-induced phenomena. [37][38][39][40][41][42][43][44][45][46][47][48]…”

Section: Discussionmentioning

confidence: 99%

“…It is noted that the selection of the matrix X in (11) would affect the feasibility of the inequalities in Theorem 3. For practicality, we can simply choose X = 1− I, where the scalar ∈ (0, 1) can be determined by checking the feasibility of the LMIs (36) to (39) and (44). As per the above discussion, we summarize the design procedure of the observer-based SMC law (13) as follows:…”

Section: Theoremmentioning

confidence: 99%

Observer‐based sliding mode control for state‐saturated systems under weighted try‐once‐discard protocol

Song

Wang

Niu

et al. 2020

Intl J Robust & Nonlinear

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In this article, the sliding mode control (SMC) problem is investigated for a class of networked state-saturated systems. In the underlying system, the system states are saturated and unmeasured, and the communication along the sensor-to-controller channel is regulated by the weighted try-once-discard protocol under which only one sensor node is permitted to transmit data at each instant. The key issue of the addressed problem is how to design both the state observer and the observer-based sliding mode controller in the presence of protocol scheduling and state saturation. A token-dependent state-saturated observer is first constructed, and a desired SMC law is then designed with aid of the updating rule for the actually received measurement. Sufficient conditions are established to guarantee both the asymptotic stability of the SMC system and the reachability of the specified sliding surface. The algorithm for acquiring the desired controller gain matrices is proposed in terms of a convex optimization problem. Finally, the effectiveness of the developed observer-based SMC design scheme is verified by a simulation example. K E Y W O R D S communication protocol, networked control systems, sliding mode control, state saturation, weighted try-once-discard protocol How to cite this article: Song J, Wang Z, Niu Y, Hu J. Observer-based sliding mode control for state-saturated systems under weighted try-once-discard protocol.

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“…To enhance wireless sensors' computation capabilities, cloud computing was presented as an effective technology, with which sensor devices could offload computational-intensive tasks to cloud servers for computing [4][5][6]. Nevertheless, traditional central clouds are usually remotely located…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of Fog Computing Assisted Wireless Powered Networks: Joint Energy and Time Minimization

et al. 2019

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This paper studies the optimal design of the fog computing assisted wireless powered network, where an access point (AP) transmits information and charges an energy-limited sensor device with Radio Frequency (RF) energy transfer. The sensor device then uses the harvested energy to decode information and execute computing. Two candidate computing modes, i.e., local computing and fog computing modes, are considered. Two multi-objective optimization problems are formulated to minimize the required energy and time for the two modes, where the time assignments and the transmit power are jointly optimized. For the local computing mode, we obtain the closed-form expression of the optimal time assignment for energy harvesting by solving a convex optimization problem, and then analyze the effects of scaling factor between the minimal required energy and time on the optimal time assignment. For the fog computing mode, we derive closed-form and semi-closed-form expressions of the optimal transmit power and time assignment for offloading by adopting the Lagrangian dual method, the Karush-Kuhn-Tucker (KKT) conditions and Lambert W Function. Simulation results show that, when the sensor device has poor computing capacity or when it is far away from the AP, the fog computing mode is the better choice; otherwise, the local computing is preferred to achieve a better performance.Electronics 2019, 8, 137 2 of 17 and far away from wireless sensor devices, thus offloading computation tasks to centralized cloud servers may results in heavy access burden and transmission delay. Therefore, a new communication and computing paradigm called fog computing was presented to extend cloud computing from network center to network edge [7,8]. Compared with cloud servers, fog servers are closer to user terminal equipments. Thus, fog computing is able to achieve lower response delay. Moreover, by offloading computation intensive tasks from ultra-low-power sensor devices to fog servers, the energy consumption of sensors may be saved and computation capabilities of wireless sensors is capable of being supplemented.Apart from the limited computation capacity of ultra-low-power sensor devices, another key issue is how to supply sustainable and stable power to sensor devices, because IoT sensor devices are often widely deployed in large-scale networks and powered by small-size batteries with limited energy storage capacities. To release the cost and risk of battery replacement, in large-scale applications and toxic environment, wireless power transfer (WPT) was presented as a promising solution. As for wireless power transfer, there are three different kinds of technologies, i.e., induction coupling, magnetic resonance coupling and RF radiation. Among them, induction coupling and magnetic resonance coupling are near-field power transfer technologies, which basically are able to charge the devices in the range of tenths of watts, over short distances of up to one meter [9]. Particularly, induction coupling needs tight alignment of the coils of chargers ...

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Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks

Cited by 55 publications

References 21 publications

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

Significance-based multi-scale method for network community detection and its application in disease-gene prediction

Observer‐based sliding mode control for state‐saturated systems under weighted try‐once‐discard protocol

Multi-Objective Optimization of Fog Computing Assisted Wireless Powered Networks: Joint Energy and Time Minimization

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