Clustering of graphs using pseudo-guided random walk

Halim, Zahid; Sargana, Hussain Mahmood; Aadam, None; Uzma,; Waqas, Muhammad

doi:10.1016/j.jocs.2020.101281

Cited by 16 publications

(4 citation statements)

References 29 publications

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“…This algorithm finds communities by performing random walks. The basic idea is that if you perform random walks on the graph, then short walks are likely to stay in the same community because only a few edges lead outside a given community [409], [410]. In power systems, Walktrap can help identify tightly interconnected groups of assets or nodes.…”

Section: ) Walktrapmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies.INDEX TERMS Clustering algorithms, K-means clustering, power systems. FIGURE 3. K-means clustering and power system bibliometric trend per country.

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Section: ) Walktrapmentioning

confidence: 99%

K-Means and Alternative Clustering Methods in Modern Power Systems

Miraftabzadeh,

Colombo,

Longo

et al. 2023

IEEE Access

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“…The random walk explanation and the partition for SIS components This section introduces the principle of random walk, which is considered an effective method of graph clustering. By considering their importance, this principle can be effective in terms of randomly shifting to its neighbors [73]. We set u ∈ V, with u denoting the probability proportional to ω e , and randomly chose v as a vertex belonging to hyperedge e uniformly.…”

Section: The Smart Collaboration and Sustainable Correlation Evaluati...mentioning

confidence: 99%

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

et al. 2023

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Compared with the conventional industrial product–service system, the smart industrial service ecosystem (SISE) mentioned in this study contains more service activity according to the characteristics of the industrial context, participation of various stakeholders and smart interconnected technologies. This study proposes a detailed modularization design framework for SISE, which can be referenced in various industrial contexts. Firstly, the context-based smart industrial service identification blueprint (SISIB) is proposed to describe the operation model of SISE and identify the service components. The SISIB can ensure that the designers understand the service and work process of the system and improve or carry out the smart industrial service (SIS) component identification. In the case of this article, SIS components from different industrial levels can be systematically identified. Secondly, smart collaboration and sustainable development principles are proposed for measuring the correlation degree among the service components. Considering the complexity and multi-level distribution nature of service components, the hyperedge concept is presented to realize the correlation comparison among the service components, and the evaluation linguistics is applied to handle the decision uncertainties. With this method, the effective correlation comparison between service components can be formed with few hyperedges. Thirdly, the hypergraph clustering theory is applied to define the SISE service module partition. The triangular fuzzy number is first used in hyperedge strength evaluation to comply with the vague linguistics from service design experts. The normalized hypergraph cut principle is realized using the K nearest neighbors (kNN) algorithm, and with this method, the new unified hypergraph and related Laplace matrix can be obtained. Then, the relevant eigenvalue of that Laplace matrix is gained, and the component clustering visualization is realized using the k-means algorithm. After the clustering is performed, several modular design schemes can be gained. In order to select the best modularization scheme, we referenced the modularity concept and realized the quality measurement for the modular design using hypergraph modularity criteria. Regarding these three steps, a detailed modularization case study for a renewable electricity service ecosystem design is presented to verify the viability and feasibility of the study in service modular design. The result showed that the framework in this study can realize the visible and clearance service component identification in a smart connected multi-level industrial context. The modular design scheme based on hypergraph can also achieve high modularity with a more convenient correlation evaluation.

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“…The need for low computational cost when optimizing nondeterministic polynomial (NP) problems is unavoidable that the processes to be calculated (Halim et al 2021) (Halim and Rehan 2020). The intrinsic time requirements of algorithms play an important role in computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Battle royale optimizer with ring neighborhood topology

Akan

Zálabský

Shirini

et al. 2023

Preprint

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Recently, battle royale optimizer (BRO), a game-based metaheuristic search algorithm, has been proposed for use in continuous optimization, inspired by a genre of digital games known as “battle royale.” In BRO, each individual chooses the nearest opponent as a competitor. For this purpose, the Euclidean distance between individuals is calculated. This interaction corresponds to an increase in computational complexity by a factor of \(n\). For the purpose of improving the computational complexity of BRO, a modified methodology is proposed using a ring topology, namely, BRO-RT. In the modified version, a set of individuals is arranged in a ring such that each has a neighborhood comprising a number of individuals to its left and right. Instead of a pairwise comparison with all individuals in the population, the best individual among the left and right neighborhoods is selected as the competitor. The proposed scheme has been compared with the original BRO and six popular optimization algorithms. All algorithms are evaluated by applying them to thirteen unimodal and multimodal benchmark optimization functions from CEC2008 and CEC2010. Experimental results show that the BRO-RT algorithm is competitive with or superior to the other seven methods. In addition, the compression spring design problem was used to estimate the ability of the proposed method to solve real-world engineering problems. These results demonstrate that BRO-RT yields promising results when applied to real-world engineering problems. Finally, while BRO is ranked first, and BRO-RT second, they achieved competitive results; BRO-RT has the advantages of lower computational complexity and faster run times than the original BRO algorithm.

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Clustering of graphs using pseudo-guided random walk

Cited by 16 publications

References 29 publications

K-Means and Alternative Clustering Methods in Modern Power Systems

K-Means and Alternative Clustering Methods in Modern Power Systems

Modularization Design for Smart Industrial Service Ecosystem: A Framework Based on the Smart Industrial Service Identification Blueprint and Hypergraph Clustering

Battle royale optimizer with ring neighborhood topology

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