An ideal point based many-objective optimization for community detection of complex networks

Tahmasebi, Sahar; Moradi, Parham; Ghodsi, Siamak; Abdollahpouri, Alireza

doi:10.1016/j.ins.2019.06.010

Cited by 30 publications

(8 citation statements)

References 39 publications

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“…They generally tend to produce a solution with fixed property for the community structure [25]. Therefore, the problem has been reformulated as a multi-objective optimization function with two conflict objectives to produce a set of tradeoff solutions [26,13,27,28,29,15,30,31,32]. Furthermore, Shi et al [33] proposed an algorithm to select the objective functions for a multiobjective evolutionary algorithm (MOEA) and they concluded that optimizing two contradictory objectives which have negative correlation outperformed those evolutionary algorithms with single objective functions.…”

Section: Motivationmentioning

confidence: 99%

A Multi-Objective Evolutionary Algorithm with Neighbour NodeCentrality for Community Detection in Complex Networks

Abbood

Attea

Hasan

et al. 2022

Preprint

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The field of identifying natural partitions in complex networks has witnessed enormous attention in recent years. However, the mathematical definition to unfold accurate structure for communities is still in need of more investigation. Further, the literature lacks any driving strategy to claim which mathematical definition is to be honored. Our contribution in this paper is threefold. First, we introduce a new score model for the community detection problem. Unlike other state-of-theart models, the proposed model aims to reflect the atom relationships of the nodes rather than that of the communities. The proposed model includes two new contradictory objective functions. The first objective is ruled in favor of tightly grouping the neighbours for each node within one community. The second objective, on the other hand, attempts to disturb the maximum number of external interactions for each node with respect to the node’s internal neighbours. The second contribution comes with designing a new algorithm, a multi-objective evolutionary algorithm based community detection in complex networks (MOEA-CD). The contradictory objectives composing the proposed score model are optimized simultaneously in the MOEA-CD. We also introduce a new local heuristic search operator, namely, Neighbour Node Centrality (NNC) strategy. The strategy is used in the MOEA-CD algorithm to improve its performance. The third contribution comes with proposing a new evaluation technique to validate the quality of the existing and the proposed score functions. The proposed technique is based on a random node migration strategy applied on the ground-truth partition for the network. Empirical evaluation of the proposed model against the state-of-the-art models clarifies that the proposed model is generally more correlated to represent the Normalised Mutual Information (NMI) between a candidate partition and the correct partition. Further, extensive experiments on both real-world and synthetic benchmarks of different complexities demonstrate that the proposed model can reveal more accurate community structures in comparison to the state-of-the-art models. The results also demonstrate the ability of the proposed (NNC) strategy to improve the detection accuracy of all tested models.

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Section: Motivationmentioning

confidence: 99%

A Multi-Objective Evolutionary Algorithm with Neighbour NodeCentrality for Community Detection in Complex Networks

Abbood

Attea

Hasan

et al. 2022

Preprint

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“…In power grids, community structures are often used in power system recovery [14,15], reactive power network partitioning [16], and coherency-based dynamic equivalence [17]. Several methods have been commonly used in previous studies to detect communities in networks, such as hierarchical clustering [18,19], modular optimization [20,21], machine learning, and other algorithms [22][23][24]. Existing studies and algorithms may only focus on the physical structure of the power grid and ignore its functions [25], thereby failing to fully reflect the electrical characteristics of the power grid.…”

Section: Link and Leverage Analyses A Link Analysis -Community Detectionmentioning

confidence: 99%

Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

Wang¹,

Wang

Alidaee

et al. 2022

IEEE Access

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Smart grid construction provides the basic conditions for grid connection of renewable energy power generation. However, the grid connection of large-scale intermittent renewable energy sources increases the complexity of operational control of power systems. With the increase in intermittent renewable energy grid connections, distribution system operators must optimize and integrate these new participants to ensure the flexibility and stability of smart grids. Dividing the smart grid into logical clusters helps to overcome the problems caused by intermittent renewable energy grid connections. In this study, we propose a 2-step modeling approach that includes both link and leverage analyses to detect the network partitioning and assess the stability of the smart grids. Our experimental results of the link analysis show that, despite the identical scores in modularity and Silhouette Coefficients (SC), the total computational time of the linear programming model for linkage (CD1) is 29.8% shorter than that of the quadratic programming model for linkage (CD2) on 7 networks with fewer than 200 nodes, whereas CD2 is 29.5% faster than CD1 on 19 larger networks with more than 200 nodes. The leverage results of benchmark networks indicate that the computational time of each instance with the proposed linear programming model for leverage (ID1) and quadratic programming model for leverage (ID2) was substantially reduced, and the Critical Node Problem (CNP) results of mediumand large-scale networks were better than those reported in the literature, which play a significant role in smart grid optimization.INDEX TERMS Intermittent renewable energy, smart grid, link analysis, leverage analysis, grid partitions I. INTRODUCTION

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“…In order to assess the reliability of the cloud model, the ideal point and gray relation projection methods were compared in this paper [45][46][47]. The evaluation results calculated by different methods are shown in Table 8.…”

Section: Risk Level Assessmentmentioning

confidence: 99%

A Normal Cloud Model-Based Method for Risk Assessment of Water Inrush and Its Application in a Super-Long Tunnel Constructed by a Tunnel Boring Machine in the Arid Area of Northwest China

Wang

Shi

et al. 2020

Water

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Tunnel water inrush is complex, fuzzy, and random, and it is affected by many factors, such as hydrology, geology, and construction. However, few papers have considered the impact of dynamic monitoring on water inrush in previous research. In this study, considering geological, hydrological, and construction factors, as well as dynamic monitoring, a new multi-index evaluation method is proposed to analyze the risk of tunnel water inrush based on the normal cloud model. A new weight algorithm combining analytic hierarchy process and entropy method is used to calculate the index weight. The certainty degree of each evaluation index belonging to the corresponding cloud can be obtained by the cloud model theory. The final level of tunnel water inrush is determined via the synthetic certainty degree. The proposed method is applied to analyze the risk of water inrush in the SS (Shuang-san) tunnel constructed by a tunnel boring machine in the arid area of Northwest China. The evaluation results are not only basically identical to the results calculated by the ideal point and gray relation projection methods, but also agree well with the actual excavation results. This demonstrates that this new risk assessment method of water inrush has high accuracy and feasibility. Simultaneously, it also provides a new research idea to analyze the probability of tunnel water inrush and can provide a reference for related projects.

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An ideal point based many-objective optimization for community detection of complex networks

Cited by 30 publications

References 39 publications

A Multi-Objective Evolutionary Algorithm with Neighbour NodeCentrality for Community Detection in Complex Networks

A Multi-Objective Evolutionary Algorithm with Neighbour NodeCentrality for Community Detection in Complex Networks

Leveraging the Influence of Power Grid Links in Renewable Energy Power Generation

A Normal Cloud Model-Based Method for Risk Assessment of Water Inrush and Its Application in a Super-Long Tunnel Constructed by a Tunnel Boring Machine in the Arid Area of Northwest China

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