Traveling salesman problems with PageRank Distance on complex networks reveal community structure

Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

doi:10.1016/j.physa.2016.07.050

Cited by 10 publications

(8 citation statements)

References 35 publications

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“…A comprehensive review of complex networks in evolutionary computation has been conducted by Jiang, Liu & Wang (2016) , while Pizzuti (2017) provides a systematic review of evolutionary algorithms that incorporate complex networks. These works offer an overview of how complex networks can be integrated into evolutionary computation.…”

Section: Methodsmentioning

confidence: 99%

“…Several studies have focused on exploring the properties of complex networks in relation to solving optimization problems. Additionally, there have been investigations centered around community detection ( Liu, Liu & Jiang, 2014 ) and PageRank distance ( Jiang, Liu & Wang, 2016 ). Moreover, according to Llanos-Mosquera et al (2022) , properties of small-world complex networks, such as the clustering coefficient and average path length, have been evaluated.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Evolutionary algorithms guided by Erdős–Rényi complex networks

Bucheli,

Solarte Pabón,

Ordoñez

2024

PeerJ Computer Science

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This article proposes an evolutionary algorithm integrating Erdős–Rényi complex networks to regulate population crossovers, enhancing candidate solution refinement across generations. In this context, the population is conceptualized as a set of interrelated solutions, resembling a complex network. The algorithm enhances solutions by introducing new connections between them, thereby influencing population dynamics and optimizing the problem-solving process. The study conducts experiments comparing four instances of the traditional optimization problem known as the Traveling Salesman Problem (TSP). These experiments employ the traditional evolutionary algorithm, alternative algorithms utilizing different types of complex networks, and the proposed algorithm. The findings suggest that the approach guided by an Erdős–Rényi dynamic network surpasses the performance of the other algorithms. The proposed model exhibits improved convergence rates and shorter execution times. Thus, strategies based on complex networks reveal that network characteristics provide valuable information for solving optimization problems. Therefore, complex networks can regulate the decision-making process, similar to optimizing problems. This work emphasizes that the network structure is crucial in adding value to decision-making.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Evolutionary algorithms guided by Erdős–Rényi complex networks

Bucheli,

Solarte Pabón,

Ordoñez

2024

PeerJ Computer Science

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“…The main goal of community discovery is to partition the network into communities. A large number of algorithms based on different disciplines, such as mathematics, biology, computer science and sociology, have been presented to discover communities in the network [9], [15], [29], [30]- [32].…”

Section: Related Workmentioning

confidence: 99%

A Memetic Algorithm for Community Detection in Bipartite Networks

2019

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Bipartite networks belong to the category of complex networks, whose vertices can be divided into two separated vertex sets, so that there are no edges between vertices in the same set, and edges only exist between vertices in different sets. In the past decades, although community discovery in one-mode networks has been deeply explored, the detection of communities in bipartite networks has not been widely studied. In this paper, we present a new memetic algorithm named MATMCD-BN for community detection in bipartite networks with two types of nodes in the community. Firstly, we put forward a new initialization method for population initialization of memetic algorithm for bipartite network communities discovery, which can expedite the convergence speed of this algorithm. Secondly, besides using traditional mutation operator, we propose a new crossover operator (called two-way random crossover operator in this paper) and a new mutation operator (called mutation operator 2 in this paper), which are helpful to improve the accuracy of the solution and accelerate the convergence speed of the proposed algorithm. Finally, we develop a local search method, which can make the solution approach the global optimal solution quickly and jump out of the local optimal solution with a certain probability. As far as we know, the proposed MATMCD-BN is the first memetic algorithm (MA) applied to community detection in bipartite networks with two types of nodes in the community. In order to confirm the performance of this algorithm, we have done a lot of experiments using synthetic and real social networks. The experimental results demonstrate that the presented method is effective and promising for bipartite network community identification.

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“…Guimerà et al [19] used network analysis to identify the most important airports in the world, based on their centrality measures. On the other hand, several studies have also analysed the global air travel network using metrics such as degree centrality, betweenness centrality, and PageRank algorithm [20][21][22][23]. These studies have provided insights into the connectivity and centrality of different airports and regions in the global air travel network.…”

Section: Introductionmentioning

confidence: 99%

Understanding the impact of network structure on air travel pattern at different scales

Huynh,

Ng,

Toh

et al. 2024

PLoS ONE

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This study examines the global air travel demand pattern using complex network analysis. Using the data for the top 50 airports based on passenger volume rankings, we investigate the relationship between network measures of nodes (airports) in the global flight network and their passenger volume. The analysis explores the network measures at various spatial scales, from individual airports to metropolitan areas and countries. Different attributes, such as flight route length and the number of airlines, are considered in the analysis. Certain attributes are found to be more relevant than others, and specific network measure models are found to better capture the dynamics of global air travel demand than others. Among the models, PageRank is found to be the most correlated with total passenger volume. Moreover, distance-based measures perform worse than the ones emphasising the number of airlines, particularly those counting the number of airlines operating a route, including codeshare. Using the PageRank score weighted by the number of airlines, we find that airports in Asian cities tend to have more traffic than expected, while European and North American airports have the potential to attract more passenger volume given their connectivity pattern. Additionally, we combine the network measures with socio-economic variables such as population and GDP to show that the network measures could greatly augment the traditional approaches to modelling and predicting air travel demand. We’ll also briefly discuss the implications of the findings in this study for airport planning and airline industry strategy.

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Traveling salesman problems with PageRank Distance on complex networks reveal community structure

Cited by 10 publications

References 35 publications

Evolutionary algorithms guided by Erdős–Rényi complex networks

Evolutionary algorithms guided by Erdős–Rényi complex networks

A Memetic Algorithm for Community Detection in Bipartite Networks

Understanding the impact of network structure on air travel pattern at different scales

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