Community structures and role detection in music networks

Abstract

We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

Using global diversity and local features to identify influential social network spreaders

“…However, broad spreading ranges were observed for nodes located in the network's core layers [2]. In addition, the ‫ܧ‬ ሺܺ ሻ normalized global diversity values produced by our proposed method were similar to participation coefficients reported by Teitelbaum et al [31], and the high global diversity of nodes that we observed were similar in terms of role with connector Figure 4; Bold, measurement result is inside the leading network group; Subscript, rank of network in the measurement.…”

“…For example, global algorithms such as community detection algorithms can be used to analyze and obtain global information on community network structures, and to determine how factors such as position and node role [31] affect the degree to which spreaders distribute information or diseases throughout a network. We also plan to study strategies associated with multiple initial spreaders in networks.…”

Multiplex networks of musical artists: The effect of heterogeneous inter-layer links

“…Other studies have focused on the understanding of how collaboration between artists arises and its 2 influence in music similarity. For example, in [12] community detection algorithms were used to identify the role of musical leaders both in collaboration and similarity networks, distinguishing between local leaders, whose influence was restricted to a specific musical genre and connector leaders linking different musical styles. More recently, Park et al [13] investigated the cooccurence of classical music composers in CDs (compact discs) with the aim of understanding the centrality of western classical composers.…”

Visualizing Networks of Music Artists With Rama

“…Given specific types of artist-related data one wants to visualize, a specific mapping must be done onto these data "containers". Nodes (or vertices) and edges are also central to the science of complex networks (Barabási, 2002), and a number of works in this field have recently brought some light onto the manifold intertwinements of musical artists networks (Cano et al, 2006), (Teitelbaum et al, 2008). There are several applications for visualizing artist networks as twodimensional connected graphs.…”