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Communities representing groups of agents with similar interests or functions are one of the essential features of complex networks. Finding communities in real-world networks is critical for analyzing complex systems in various areas ranging from collaborative information to political systems. Given the different characteristics of networks and the capability of community detection in handling a plethora of societal problems, community detection methods represent an emerging area of research. Contributing to this field, the authors propose a new community detection algorithm based on the hybridization of node and link granulation.

The proposed algorithm utilizes a rough set-theoretic concept called closure on networks. Initial sets are constructed by using neighborhood topology around the nodes as well as links and represented as two different categories of granules. Subsequently, the authors iteratively obtain the constrained closure of these sets. The authors use node mutuality and link mutuality as merging criteria for node and link granules, respectively, during the iterations. Finally, the constrained closure subsets of nodes and links are combined and refined using the Jaccard similarity coefficient and a local density function to obtain communities in a binary network.

Extensive experiments conducted on twelve real-world networks followed by a comparison with state-of-the-art methods demonstrate the viability and effectiveness of the proposed algorithm.

The study also contributes to the ongoing effort related to the application of soft computing techniques to model complex systems. The extant literature has integrated a rough set-theoretic approach with a fuzzy granular model (Kundu and Pal, 2015) and spectral clustering (Huang and Xiao, 2012) for node-centric community detection in complex networks. In contributing to this stream of work, the proposed algorithm leverages the unexplored synergy between rough set theory, node granulation and link granulation in the context of complex networks. Combined with experiments of network datasets from various domains, the results indicate that the proposed algorithm can effectively reveal co-occurring disjoint, overlapping and nested communities without necessarily assigning each node to a community.

This study carries important practical implications for complex adaptive systems in business and management sciences, in which entities are increasingly getting organized into communities (Jacucci et al., 2006). The proposed community detection method can be used for network-based fraud detection by enabling experts to understand the formation and development of fraudulent setups with an active exchange of information and resources between the firms (Van Vlasselaer et al., 2017). Products and services are getting connected and mapped in every walk of life due to the emergence of a variety of interconnected devices, social networks and software applications.

The proposed algorithm could be extended for community detection on customer trajectory patterns and design recommendation systems for online products and services (Ghose et al., 2019; Liu and Wang, 2017). In line with prior research, the proposed algorithm can aid companies in investigating the characteristics of implicit communities of bloggers or social media users for their services and products so as to identify peer influencers and conduct targeted marketing (Chau and Xu, 2012; De Matos et al., 2014; Zhang et al., 2016). The proposed algorithm can be used to understand the behavior of each group and the appropriate communication strategy for that group. For instance, a group using a specific language or following a specific account might benefit more from a particular piece of content than another group. The proposed algorithm can thus help in exploring the factors defining communities and confronting many real-life challenges.

This work is based on a theoretical argument that communities in networks are not only based on compatibility among nodes but also on the compatibility among links. Building up on the aforementioned argument, the authors propose a community detection method that considers the relationship among both the entities in a network (nodes and links) as opposed to traditional methods, which are predominantly based on relationships among nodes only.

This paper aims to assess the representation of women of color (WOC) in the top 3 fashion magazines and explore the implications of underrepresentation within marketing communications. The authors draw from diffusion theory and marketplace omission and commission to situate the research focus and highlight its application to the study findings.

A content analysis was conducted on 481 cover models on the top three fashion magazines of 2018 – Vogue, Cosmopolitan and Vanity Fair during 2006–2018.

The findings indicate WOC are underrepresented despite the strides of inclusion in the marketplace in America during a postracial period. Representation is as follows: white – 412 (86%); black – 41 (9%); Latina – 19 (3.9%); biracial 7 (1.5%); Asian – 1 (0.2%); and Native American – 1 (0.2%). Latina models had the lowest representation. Native and Asian women were completely excluded. When they do appear, black and Latina cover models are more likely than white models to be shown wearing sexually suggestive attire.

This study makes four recommendations to promote antiracism in marketing: diversify staff hiring and editorial decision-makers for public-facing talent; solicit counsel from multicultural marketing agencies; create antiracist marketing curriculum; and cultivate a pipeline of diverse talent for future hiring.

The originality of this paper centers its contribution to the dearth research investigating representation implications within the fashion marketing industry during an alleged post-racial period, and a longer time span. It also presents structured antiracist marketing solutions to mitigate underrepresentation.