Social Network Analysis in Professional e-Recruitment

Toteva, Kostadinka; Gourova, Elissaveta

doi:10.1007/978-3-642-23163-6_11

Cited by 3 publications

(3 citation statements)

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“…In an information-driven society, where knowledge and skills are recognized as an important resource for growth and competitiveness, human resource management (HRM) becomes a business process of strategic importance, both for the design and implementation of corporate strategy, motivation, recruitment and retention of highly qualified candidates (Toteva and Gourova, 2011). In such a defined business environment, social networks sites (SNS) enable connecting candidates and companies.…”

Section: Introductionmentioning

confidence: 99%

“…This paper presents an alternative methodological approach for the preselection of candidates in the social recruiting process by using SNA. Using the SNA methodology, it is possible to analyze the structure of the social network through concepts from graph theory and network analysis, with the help of defined mathematical models and clustering algorithms (Toteva and Gourova, 2011). Based on the definition of SNA methodology (Milovanović et al , 2019), it can be concluded that the greatest contribution of this methodology in the process of recruiting candidates is in the phase of preselection of qualified people for a certain job position.…”

Section: Introductionmentioning

confidence: 99%

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Social recruiting: an application of social network analysis for preselection of candidates

Milovanović

Bogdanović

Лабус

et al. 2022

DTA

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PurposeThe paper aims to studiy social recruiting for finding suitable candidates on social networks. The main goal is to develop a methodological approach that would enable preselection of candidates using social network analysis. The research focus is on the automated collection of data using the web scraping method. Based on the information collected from the users' profiles, three clusters of skills and interests are created: technical, empirical and education-based. The identified clusters enable the recruiter to effectively search for suitable candidates.Design/methodology/approachThis paper proposes a new methodological approach for the preselection of candidates based on social network analysis (SNA). The defined methodological approach includes the following phases: Social network selection according to the defined preselection goals; Automatic data collection from the selected social network using the web scraping method; Filtering, processing and statistical analysis of data. Data analysis to identify relevant information for the preselection of candidates using attributes clustering and SNA. Preselection of candidates is based on the information obtained.FindingsIt is possible to contribute to candidate preselection in the recruiting process by identifying key categories of skills and interests of candidates. Using a defined methodological approach allows recruiters to identify candidates who possess the skills and interests defined by the search. A defined method automates the verification of the existence, or absence, of a particular category of skills or interests on the profiles of the potential candidates. The primary intention is reflected in the screening and filtering of the skills and interests of potential candidates, which contributes to a more effective preselection process.Research limitations/implicationsA small sample of the participants is present in the preliminary evaluation. A manual revision of the collected skills and interests is conducted. The recruiters should have basic knowledge of the SNA methodology in order to understand its application in the described method. The reliability of the collected data is assessed, because users provide data themselves when filling out their social network profiles.Practical implicationsThe presented method could be applied on different social networks, such as GitHub or AngelList for clustering profile skills. For a different social network, only the web scraping instructions would change. This method is composed of mutually independent steps. This means that each step can be implemented differently, without changing the whole process. The results of a pilot project evaluation indicate that the HR experts are interested in the proposed method and that they would be willing to include it in their practice.Social implicationsThe social implication should be the determination of relevant skills and interests during the preselection phase of candidates in the process of social recruitment.Originality/valueIn contrast to previous studies that were discussed in the paper, this paper defines a method for automatic data collection using the web scraper tool. The described method allows the collection of more data in a shorter period. Additionally, it reduces the cost of creating an initial data set by removing the cost of hiring interviewers, questioners and people who collect data from social networks. A completely automated process of data collection from a particular social network stands out from this model from currently available solutions. Considering the method of data collection implemented in this paper, the proposed method provides opportunities to extend the scope of collected data to implicit data, which is not possible using the tools presented in other papers.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Social recruiting: an application of social network analysis for preselection of candidates

Milovanović

Bogdanović

Лабус

et al. 2022

DTA

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“…Another study using Pearson's correlation coefficient showed that social networks had a negative impact on the academic performance of medical students [22]. Social networks analysis (SNA) is also used in recruitment to identify potentially successful future employees [23] [24] or to recommend suitable vacancies [25]. In our previous work, we built a model based on the use of feed-forward neural networks to predict professional success on the basis of data from user profiles in VKontakte social network [26].…”

mentioning

confidence: 99%

Online social network user performance prediction by graph neural networks

Gafarov

Berdnikov²,

Ustin³

2022

Int. J. Adv. Intell. Informatics

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Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN) open up a wide range of research opportunities for scientists. Much research conducted in recent years using graph neural networks (GNN) has shown their advantages over conventional deep learning. In particular, the use of graph neural networks for online social network analysis seems to be the most suitable. In this article we studied the use of graph convolutional neural networks with different convolution layers (GCNConv, SAGEConv, GraphConv, GATConv, TransformerConv, GINConv) for predicting the user’s professional success in VKontakte online social network, based on data obtained from his profiles. We have used various parameters obtained from users’ personal pages in VKontakte social network (the number of friends, subscribers, interesting pages, etc.) as their features for determining the professional success, as well as networks (graphs) reflecting connections between users (followers/ friends). In this work we performed graph classification by using graph convolutional neural networks (with different types of convolution layers). The best accuracy of the graph convolutional neural network (0.88) was achieved by using the graph isomorphism network (GIN) layer. The results, obtained in this work, will serve for further studies of social success, based on metrics of personal profiles of OSN users and social graphs using neural network methods.

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Job seeker to vacancy matching using social network analysis

Chala

Fathi

2017

2017 IEEE International Conference on Industrial Technology (ICIT)

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Social Network Analysis in Professional e-Recruitment

Cited by 3 publications

References 3 publications

Social recruiting: an application of social network analysis for preselection of candidates

Social recruiting: an application of social network analysis for preselection of candidates

Online social network user performance prediction by graph neural networks

Job seeker to vacancy matching using social network analysis

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