An approach of community evolution based on gravitational relationship refactoring in dynamic networks

Yin, Guisheng; Chi, Kuo; Dong, Yafei; Dong, Hairong

doi:10.1016/j.physleta.2017.01.059

Cited by 17 publications

(12 citation statements)

References 22 publications

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“…The dynamic community evolving algorithm (GCEA) [38] reveals gravity relationships by considering only the degrees of each node in a dynamic social network. They only set g to 1, and m i and m j is the degree of node i and node j, respectively.…”

Section: A Gravity Relationship Of the Microblog Communitymentioning

confidence: 99%

“…The returning probability of node u t i is n/N , and the returning probability of edge e t j→i is n /N . To determine the random walk number, Yin et al [38] used a probability function to calculate the random walk step number. They set up the following random walk: a ∈ (0, 1), b = f (x), x is the current walk number of the walking user, and f (x) is a probability function used to determine whether to move on.…”

Section: Gravity Tendencymentioning

confidence: 99%

“…Extracting the evolution behaviors from community C t i to C t+1 i helps us analyze the evolving trends of communities. Previous research [33], [38] defined seven explicit evolution behaviors of communities.…”

Section: Evolution Behavior Extractionmentioning

confidence: 99%

“…We compare our method with other classic community evolution methods, such as the LDM-CET (Local Dynamic Method for Community Evolution Track) [33], GCEA [38] and iLCD (improvement Local Community Detecion) [42]. Fig.…”

Section: Comparisons Of Community Evolutionmentioning

confidence: 99%

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Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Liu

2020

IEEE Access

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Microblogs are open and real-time online social network platforms used by people to make posts about their moods, experiences, and interests. It will be very significant to gather microblog users who have similar interests and hobbies into the same community. In this paper, we propose novel approaches for detecting and evolving dynamic microblog communities. First, inspired by the universal gravitation law, we redefine the gravitation relationships among microblog users. Based on the structure of the microblog social network, we define the basic nodes and their gravity tendency and propose the microblog community detection algorithm. Second, we determine the community changes in the microblog social networks at times t and t + 1 and propose a microblog community evolution algorithm. Third, we define the mutual transformation probability between communities at times t and t + 1 and propose the microblog community evolution behavior algorithm. The experiment includes a comparison and evaluation of the microblog community detection, evolution, and behavior extraction algorithms and the optimal ranges of the parameters involved in these algorithms. The experimental results indicate that our proposed algorithms have good performance compared to other benchmarking methods.

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Section: A Gravity Relationship Of the Microblog Communitymentioning

confidence: 99%

Section: Gravity Tendencymentioning

confidence: 99%

Section: Evolution Behavior Extractionmentioning

confidence: 99%

Section: Comparisons Of Community Evolutionmentioning

confidence: 99%

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Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Liu

2020

IEEE Access

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“…Community detection in temporal, evolving or adaptive networks has largely attracted network scientists' attention due to its important implications in the analysis of dynamical processes in complex networks, such as spreading and cascading dynamics, stability, synchronisation and robustness. Different types of methods and algorithms have been used, for example: the Louvain algorithm (Aynaud and Guillaume 2010), statistical null models (Bassett et al 2013;Sarzynska et al 2016), algorithms which exploit the historic community structure of the network (He et al 2017;He and Chen 2015), Markov models (Rosvall et al 2014), semidefinite programming (Tantipathananandh and Berger-Wolf 2011), gravitational relationship between nodes (Yin et al 2017), and temporal matrix factorisation (Yu et al 2017), amongst others. Machine learning techniques (Savić et al 2019;Xin et al 2017), genetic algorithms (Folino and Pizzuti 2014), consensus clustering (Aynaud and Guillaume 2010) and tensor factorisation (Araujo et al 2014;Gauvin et al 2014) have only recently been used for the detection of communities in temporal networks.…”

Section: Introductionmentioning

confidence: 99%

Evolution of communities of software: using tensor decompositions to compare software ecosystems

2019

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Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software "ecosystems" are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as GitHub, so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package event-stream and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers-for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted-may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.

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A New Method of High-Speed Railway Station Planning Based on Network Evolution

Chengchen

Wang

Jia

et al. 2020

Lecture Notes in Electrical Engineering

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An approach of community evolution based on gravitational relationship refactoring in dynamic networks

Cited by 17 publications

References 22 publications

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Detecting and Evolving Microblog Community Based on Structure and Gravity Cohesion

Evolution of communities of software: using tensor decompositions to compare software ecosystems

A New Method of High-Speed Railway Station Planning Based on Network Evolution

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