Detection of Communities in a Graph of Interactive Objects

Kolomeychenko, M. I.; Polyakov, I. V.; Chepovskiy, A. A.; Chepovskiy, Andrey

doi:10.1007/s10958-019-04168-2

Cited by 2 publications

(3 citation statements)

References 16 publications

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“…This graph can be built based on job vacancy data from job search portals. Let's use the social network model (Kolomeichenko et al, 2019) to analyze such a graph.…”

Section: Methodsmentioning

confidence: 99%

Building And Analysing A Skills Graph Using Data From Job Portals

Skobtsov¹

2022

Proceedings of the III International Conference on Economic and Social Trends for Sustainability of Modern Society – (ICEST-III

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In this paper, the authors consider the process of building and analyzing a skills graph based on vacancies data from the job search portal. The connection between vacancies and skills is identified and formalized as labor market and employers' requirements to employee's qualification in the intellectual educational ecosystem model. The authors describe the collector program which can fetch vacancies data from job search sites with an example of site HeadHunter. Jupyter Notebook interactive notebooks and different Python-based libraries, such as Pandas and Numpy, are used for data processing. As a result, a skills graph is built and stored in the neo4j graph database platform. With the help of the Gephi application, the next properties of this graph are determined: degree distribution, the eigenvector centrality, closeness centrality, betweenness centrality, PageRank coefficient, and modularity. The graph is drawn using the ForceAtlas2 layout algorithm. The modularity coefficient for the vertices is used to find the main clusters of skills. The number of such skills clusters is almost the same as the count of specialties on the example job search portal. It results in the ability to determine specialty skillsets and interspecialty skills using vacancies data from job search sites.

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“…This graph can be built based on job vacancy data from job search portals. Let's use the social network model (Kolomeichenko et al, 2019) to analyze such a graph.…”

Section: Methodsmentioning

confidence: 99%

Building And Analysing A Skills Graph Using Data From Job Portals

Skobtsov¹

2022

Proceedings of the III International Conference on Economic and Social Trends for Sustainability of Modern Society – (ICEST-III

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“…Among the first methods for communities detection in complex network are the smallest cut, hierarchical clustering and click-based methods [5]. Algorithms based on the modularity estimation (Newman-Girvan, Blondel, Radicchi [6][7][8]), the spectral properties of the graph (Donetti-Munoz [9]), the estimation of network entropy (structural and dynamic methods of Rosvall-Bergstrom) and others are now widely used [10].…”

Section: Introductionmentioning

confidence: 99%

“…Algorithms based on the modularity estimation (Newman-Girvan, Blondel, Radicchi [6][7][8]), the spectral properties of the graph (Donetti-Munoz [9]), the estimation of network entropy (structural and dynamic methods of Rosvall-Bergstrom) and others are now widely used [10]. The main disadvantage of above mentioned algorithms for identifying communities in CN, along with computational complexity and resource consumption [11], is the lack of reliable theoretically sound criterion that defined by any of them a group of nodes actually forms a community [5,12]. The "unreliability" of above algorithms has made popular the methods of visual search for communities [13,14], especially in large networks.…”

Section: Introductionmentioning

confidence: 99%

Flow approaches to community allocation in complex network systems

Polishchuk

2021

Fìz.-mat. model. ìnf. tehnol.

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The paper investigates the problem of finding communities in complex network systems, the detec-tion of which allows a better understanding of the laws of their functioning. To solve this problem, two approaches are proposed based on the use of flows characteristics of complex network. The first of these approaches consists in calculating the parameters of influence of separate subsys-tems of the network system, distinguished by the principles of ordering or subordination, and the second, in using the concept of its flow core. Based on the proposed approaches, reliable criteria for finding communities have been formulated and efficient algorithms for their detection in com-plex network systems have been developed. It is shown that the proposed approaches make it pos-sible to single out communities in cases in which the existing numerical and visual methods turn out to be disabled.

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Detection of Communities in a Graph of Interactive Objects

Cited by 2 publications

References 16 publications

Building And Analysing A Skills Graph Using Data From Job Portals

Building And Analysing A Skills Graph Using Data From Job Portals

Flow approaches to community allocation in complex network systems

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