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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.

The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

While much attention has been given to work-related factors influencing turnover intention through affective commitment, little focus has been directed to non-work factors affecting the service industry. Hence, this study aims to investigate the impact of links, fit and sacrifice, representing off-the-job embeddedness in the community, on turnover intention in the hospitality industry of Ghana: Sub-Sahara Africa using the theory of conservation of resources (COR) and social exchange. The model has been extended to include affective commitment as the mediating mechanism.

A multi-wave technique was used to collect data through a questionnaire from 341 full-time frontline hospitality employees in Ghana. The responses were analysed using AMOS software structural equation modelling.

The findings show that links, fit and sacrifice significantly influence employees’ turnover intentions. Moreover, it has been observed that affective commitment decreased the negative relationship and partly mediated the main relationship between the dimensions of off-the-job embeddedness and turnover intention.

The study’s results and academic, practical implications and limitations are discussed for future research.

This study emphasises the theory of COR to demystify community factors employees deem as valued resources, which lighten up their commitment to their organisation and decrease their intent to leave.

We propose that off-the-job embeddedness (OTJE) be reconceptualized as a separate and distinct, albeit related, construct from job embeddedness. We conceptualize OTJE as the totality of outside-work forces which keep an individual bound to his/her current geographical area and argue that this construct includes important factors which do not fall under the umbrella of “community embeddedness.” Moreover, we propose that these outside-work forces may embed individuals in their jobs either directly or indirectly (through the perceived or expressed preferences of spouses, children, and extended family). This paper identifies the key components of OJTE, addresses the measurement of OTJE, explains the relationships between job embeddedness and OTJE (and their respective components), highlights how OTJE can either amplify or counteract the effects of job embeddedness, and illustrates the direct and indirect effects of OTJE on both work-related and personal outcomes.

Encapsulates the debate on the topics of confusion in consumption and the return of community. Starting with an ethnosociological analysis structuring the passage from modernity to postmodernity around the metamorphosis of the social link, aims at clarifying and explaining the different levels of the postmodern confusion in consumption. Modernity entered history as a progressive force promising to liberate humankind from everyday obligations and traditional bonds. As a consequence, modern consumption emphasized essentially the utilitarian value (“use value”) of products and services. Postmodernity, on the contrary, can be said to crown not the triumph of individualism, but the beginning of its end with the emergence of a reverse movement of a desperate search for community. With the neo‐tribalism distinguishing postmodernity, everyday life seems to mark out the importance of a forgotten element: the social link. Consequently, postmodern consumption appears to emphasize the “linking value” of products and services. Concludes with an exploration of the implications of postmodernity for rethinking marketing with the integration of the linking value concept.

One of the techniques for improving the performance of distributed systems is data replication, wherein new replicas are created to provide more accessibility, fault tolerance and lower access cost of the data. In this paper, the authors propose a community-based solution for the management of data replication, based on the graph model of communication latency between computing and storage nodes. Communities are the clusters of nodes that the communication latency between the nodes are minimum values. The purpose of this study if to, by using this method, minimize the latency and access cost of the data.

This paper used the Louvain algorithm for finding the best communities. In the proposed algorithm, by requesting a file according to the nodes of each community, the cost of accessing the file located out of the applicant’s community was calculated and the results were accumulated. On exceeding the accumulated costs from a specified threshold, a new replica of the file was created in the applicant’s community. Besides, the number of replicas of each file should be limited to prevent the system from creating useless and redundant data.

To evaluate the method, four metrics were introduced and measured, including communication latency, response time, data access cost and data redundancy. The results indicated acceptable improvement in all of them.

So far, this is the first research that aims at managing the replicas via community detection algorithms. It opens many opportunities for further studies in this area.

Most of the existing literature on online social networks (OSNs) either focuses on community detection in graphs without considering the topic of the messages exchanged, or concentrates exclusively on the messages without taking into account the social links. The purpose of this paper is to characterise the semantic cohesion of such groups through the introduction of new measures.

A theoretical model for social links and salient topics on Twitter is proposed. Also, measures to evaluate the topical cohesiveness of a group are introduced. Inspired from precision and recall, the proposed measures, called expertise and representativeness, assess how a set of groups match the topic distribution. An adapted measure is also introduced when a topic similarity can be computed. Finally, a topic relevance measure is defined, similar to tf.idf (term-frequency, inverse document frequency).

The measures yield interesting results, notably on a large tweet corpus: the metrics accurately describe the topics discussed in the tweets and enable to identify topic-focused groups. Combined with topological measures, they provide a global and concise view of the detected groups.

Many algorithms, applied on OSN, detect communities which often lack of meaning and internal semantic cohesion. This paper is among the first to quantify this aspect, and more precisely the topical cohesion and topical relevance of a group. Moreover, the proposed indicators can be exploited for social media monitoring, to investigate the impact of a group of people: for instance, they could be used for journalism, marketing and security purposes.

By using the concept of style, the purpose of this paper is to elaborate on the notion of brand community. More specifically, it seeks to explore how style can function as a linking value in forms of communities centred on brands that emerge within the empirical context of fashion and social media.

A netnography of the content produced by 18 fashion bloggers in Sweden was conducted. Content analysis of this material was used to map how consumption objects, in terms of fashion brands, were integrated in activities taking place on blogs, and through these processes, acted as a linking value for community members.

– This paper demonstrates how fashion bloggers, together with their readers, constitute a form of community centred on style. It also shows how fashion bloggers, by combining and assembling fashion brands and products, articulate and express different style sets, and how they, together with their followers, engage in activities connected to these style ideals.

– As this study has been empirically limited to a Swedish setting, future research would benefit from findings of international expressions of communities of style.

Based on this study, strategies for managing communities of style is suggested to represent a potential source of competitive advantage for fashion firms.

In the context of the conceptual discussion about what brings members of communities together, this study provides evidence of how style can function as a linking value in the setting of consumer communities that emerge within the boundaries of fashion and social media.

Seeks to explore the notion of fashion networks, both local and global, as a means by which “products” – economic and intangible – can be exchanged and to present research findings of a network study undertaken in the Nottingham knitting industry.

Presents a study of knitting companies in the Nottingham area of the UK using social network methods to identify the structure and role of the local and global networks in which these businesses sit and shows, by describing the networks of two contrasting companies, the advantages and challenges they afford to the learning of individual businesses and other participants in the local network.

The density of the local network is presented and the role of universities and regional development agencies is shown to be important. The profiles of two‐example companies shows one type which is well connected within the local network and another type which has good global links but is not well connected locally. The implications of these two kinds of profile are discussed.

Indicates the importance of developing balanced networks, which allow the dissemination of ideas, information and norms and provide opportunities for exchange.

Challenges current ideas about fashion supply chains by exploring markets as complex networks of relationships which reflects the blurring of boundaries between firms and changing perceptions of “customers” and currencies of exchange. This paper revisits notions of markets in the context of the needs of small and medium‐sized fashion businesses and in particular focuses on their learning and development.