In this paper, we mainly do the research about the non-overlapping community discovery. We use the GN algorithm and LP algorithm to test the randomly generated network, and compare the two algorithms in two layers, wh...
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ISBN:
(纸本)9781509061266
In this paper, we mainly do the research about the non-overlapping community discovery. We use the GN algorithm and LP algorithm to test the randomly generated network, and compare the two algorithms in two layers, which are the time complexity level, and the static network's synchronization capability level. The simulation results show that the two algorithms are applicable to different scenes.
In this paper we present the first case study on collaboration network of researchers at the University of Montenegro - UoM. We identify the largest clusters or groups of scientists that are interested in the same top...
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ISBN:
(纸本)9781509022212
In this paper we present the first case study on collaboration network of researchers at the University of Montenegro - UoM. We identify the largest clusters or groups of scientists that are interested in the same topic, using girvan-newman algorithm. The results show that these clusters constantly grow over the period 2005-2015 and at the moment they occupy more than 50% authors from the UoM. It indicates that there exists increasingly collaboration between authors from the UoM network. But, according to classification of real networks the UoM network is in "sub-critical" regime, and still far away from "connected" regime. The study is limited to the authors from the UoM and we do not consider any collaboration outside of the UoM. Also, we consider only papers published in the SCI, SCIE, SSCI, A&HCI and SCOPUS categories, because they are recognized as the most important for professional career of professors at the UoM.
Implementing an inter-regional synergistic control policy for fine particulate matter (PM2.5) and ground-level ozone (O-3) could improve regional air quality. However, little is known about the effectiveness and accur...
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Implementing an inter-regional synergistic control policy for fine particulate matter (PM2.5) and ground-level ozone (O-3) could improve regional air quality. However, little is known about the effectiveness and accuracy of synergistic control region delineation. This study aimed to construct a network model and apply it to a case study of regional delineation in China at different scales to quantify the interactions between regions. Firstly, the Cumulative Risk Index (CRI) was proposed and quantified from a health risk perspective based on the daily mean PM2.5 and daily maximum 8-h average O-3 concentrations from 2015 to 2020 in China. Then, the complex network topology parameters were introduced to determine the optimal threshold for different network constructions, and the girvan-newman (GN) algorithm was used to divide the network into independent regions. Results showed that the correlation between cities is more robust than that between provinces. There are four-seven major provincial-scale regions with strong synchronicity in CRI, suggesting that PM2.5 and O-3 synergistic control policies shall be implemented jointly within these demarcated regions. Moreover, urban-scale CRI network analysis indicated that the existing key control areas (2 + 26 cities) need to be expanded to 40-50 cities and refined into seven independent urban regions. Meanwhile, the Fen-Wei Plain can be focused on six cities: Xi'an, Baoji, Xianyang, Weinan, Yuncheng, and Tongchuan. This study could improve our understanding of the synergistic control regions for PM2.5 and O-3 pollution, and the results could be used to develop joint control policies for both pollutants.
In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped...
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In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional girvan-newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.
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