Combinational Nonuniform Timeslicing of Dynamic Networks
arxiv(2024)
摘要
Dynamic networks represent the complex and evolving interrelationships
between real-world entities. Given the scale and variability of these networks,
finding an optimal slicing interval is essential for meaningful analysis.
Nonuniform timeslicing, which adapts to density changes within the network, is
drawing attention as a solution to this problem. In this research, we
categorized existing algorithms into two domains – data mining and
visualization – according to their approach to the problem. Data mining
approach focuses on capturing temporal patterns of dynamic networks, while
visualization approach emphasizes lessening the burden of analysis. We then
introduce a novel nonuniform timeslicing method that synthesizes the strengths
of both approaches, demonstrating its efficacy with a real-world data. The
findings suggest that combining the two approaches offers the potential for
more effective network analysis.
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