A Review on Python Libraries for Temporal Network Analysis
Linhares, Claudio D. G., 1990- (författare)
Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),ISOVIS; LNUC DISA,Department of Computer Science, Linnaeus University, Växjö, Sweden
Ponciano, Jean R. (författare)
University of São Paulo, Brazil
Oliveira, Martim R. (författare)
Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Department of Computer Science, Linnaeus University, Växjö, Sweden
Soares, Amilcar, Ph.D. in Computer Science (författare)
Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),ISOVIS, LNUC DISA,Department of Computer Science, Linnaeus University, Växjö, Sweden
Traina, Agma J.M. (författare)
University of São Paulo, Brazil
Kerren, Andreas, Dr.-Ing. 1971- (författare)
Linköpings universitet,Linnéuniversitetet,Institutionen för datavetenskap och medieteknik (DM),Linköping University, Sweden,ISOVIS, LNUC DISA,Tekniska fakulteten,Medie- och Informationsteknik,Linnaeus University, Sweden,iVis, INV
Context: Complex networks represent systems with non-trivial connections and are widely used in fields such as social media, biology, and transportation. Temporal networks extend this by capturing the evolution of connections over time, providing insights into event sequences and information diffusion. Analyzing these networks requires specialized tools, and Python offers a variety of libraries tailored for this purpose.Objectives: This study evaluates Python libraries designed for temporal network analysis based on multiple criteria. The aim is to assess the strengths and limitations of these tools, guide users in selecting appropriate libraries, and identify gaps for future development.Methods: A comparative analysis was conducted on selected Python libraries using predefined evaluation criteria. The assessment considered factors such as available documentation, supported metrics, visualization capabilities, supported format, uniqueness, community support, and popularity. Data were gathered from official documentation, community forums, scientific papers, and usage statistics.Results: Findings indicate that the TGX, Teneto, and PathpyG stand out, excelling in three of five criteria. Networkx-t shows balanced performance with no significant drawbacks, making it a reliable general-purpose choice. However, several tools have limitations in specific areas, such as a lack of comprehensive documentation or advanced visualization features.Conclusion: This review provides an overview of existing Python tools for temporal network analysis, offering insights into their capabilities and shortcomings. The results assist researchers and practitioners in selecting suitable libraries while highlighting areas for improvement and potential future developments in the field.
Ämnesord
NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)