A Flow-Based Node Dominance Centrality Measure for Complex Networks

Mandyam, Sridhar; Sridhar, Usha

doi:10.1007/s42979-022-01270-2

Cited by 2 publications

(1 citation statement)

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“…The similarity between DomiRank and our proposed IRDI algorithm lies in their mutual focus on both local and global information, although applied in different contexts. Mandyam Kannappan and Sridhar [16] formulated a centrality metric known as DONEX, which originates from a Pareto optimal solution addressing the collective welfare maximization problem. DONEX assesses the concepts of dominance and influence within weighted directed networks.…”

Section: Related Literaturementioning

confidence: 99%

Understanding the Influence of Genre-Specific Music Using Network Analysis and Machine Learning Algorithms

Lamichhane,

Singh,

Devkota

et al. 2023

BDCC

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This study analyzes a network of musical influence using machine learning and network analysis techniques. A directed network model is used to represent the influence relations between artists as nodes and edges. Network properties and centrality measures are analyzed to identify influential patterns. In addition, influence within and outside the genre is quantified using in-genre and out-genre weights. Regression analysis is performed to determine the impact of musical attributes on influence. We find that speechiness, acousticness, and valence are the top features of the most influential artists. We also introduce the IRDI, an algorithm that provides an innovative approach to quantify an artist’s influence by capturing the degree of dominance among their followers. This approach underscores influential artists who drive the evolution of music, setting trends and significantly inspiring a new generation of artists. The independent cascade model is further employed to open up the temporal dynamics of influence propagation across the entire musical network, highlighting how initial seeds of influence can contagiously spread through the network. This multidisciplinary approach provides a nuanced understanding of musical influence that refines existing methods and sheds light on influential trends and dynamics.

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Section: Related Literaturementioning

confidence: 99%