[Paper Review] Uncovering the Skillsets Required in Computer Science Jobs Using Social Network Analysis

Mehrdad Maghsoudi|arXiv (Cornell University)|Aug 16, 2023

Online Learning and AnalyticsComputer Science3 citations

TL;DR

This paper proposes a social network analysis approach to identify in-demand computer science skills by constructing a skill communication network from LinkedIn data and 7,777 job ads. It reveals four skill communities—Generalists, Infrastructure and Security, Software Development, and Embedded Systems—and identifies communication, English, SQL, Git, and business skills as top-impact competencies, with communication emerging as the most central skill in the labor market.

ABSTRACT

The rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the market. In this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.

Motivation & Objective

• To address the challenge of rapidly evolving skill demands in computer science by identifying critical skills required by employers.
• To understand how hard and soft skills are interconnected in the job market using network-based analysis.
• To guide individuals and organizations in skills acquisition and hiring by mapping skill centrality and community structures.
• To identify the most influential skills based on network centrality metrics in the context of computer science job postings.

Proposed method

• Collected 5,763 unique skills from LinkedIn to form the foundation of the skill network.
• Extracted skill relationships from 7,777 computer science job advertisements to model skill co-occurrence.
• Constructed a balanced communication network of skills to ensure equitable representation of skill interactions.
• Applied community detection algorithms to identify distinct clusters of interrelated skills in the job market.
• Calculated centrality indices (e.g., degree, betweenness) to rank skill importance in the network.
• Validated findings by analyzing skill communities and top-ranked skills based on network metrics.

Experimental results

Research questions

• RQ1 Which skills are most frequently co-mentioned in computer science job advertisements, indicating high market demand?
• RQ2 How are hard and soft skills interconnected in the computer science job market?
• RQ3 What are the dominant communities of skills, and how do they reflect different career tracks in computer science?
• RQ4 Which skills exhibit the highest centrality in the skill network, suggesting greater influence or importance?
• RQ5 How do communication and business skills compare in centrality to technical programming skills in job postings?

Key findings

• Communication skills were identified as the most central skill in the computer science job market, indicating their high importance across diverse roles.
• The skill network revealed four distinct communities: Generalists, Infrastructure and Security, Software Development, and Embedded Systems.
• SQL, Git, English language proficiency, and business skills ranked highly in centrality metrics, signaling strong market demand.
• Programming languages were prominent but not the most central skills, highlighting the value of broader competencies.
• The integration of soft skills like teamwork and communication was consistently observed across all skill communities.
• The balanced network model effectively captured skill interdependencies, providing a robust basis for skill prioritization in hiring and upskilling.