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The state-of-the-art methods designed for overlapping community detection are limited by their high execution time as in CPM or the need to provide some parameters like the number of communities in Bigclam and Nise_sph, which is a nontrivial information. Hence, there is a need to develop the accuracy that represents the primordial goal, where the actual state-of-the-art methods do not succeed to achieve high correspondence with the ground truth for many instances of networks. The paper aims to discuss this issue.

The authors offer a new method that explore the union of all maximum spanning trees (UMST) and models the strength of links between nodes. Also, each node in the UMST is linked with its most similar neighbor. From this model, the authors extract local community for each node, and then they combine the produced communities according to their number of shared nodes.

The experiments on eight real-world data sets and four sets of artificial networks show that the proposed method achieves obvious improvements over four state-of-the-art (BigClam, OSLOM, Demon, SE, DMST and ST) methods in terms of the F-score and ONMI for the networks with ground truth (Amazon, Youtube, LiveJournal and Orkut). Also, for the other networks, it provides communities with a good overlapping modularity.

In this paper, the authors investigate the UMST for the overlapping community detection.

This study aims to introduce and define the concept of phygital brand community (PBC). It discusses the potential conflicts that can arise from engaging in multiple PBCs and propose an enriched netnographic methodological approach to explore the role of PBC engagement overlap and its influence on the phygital experience.

Following a critical analysis of the inherent limitations of netnographic methodological approaches in the context of PBCs, this study develops an enriched netnographic research protocol that accounts for the challenges of engagement overlap among PBCs.

This study proposes two methods of analysis, namely, “participatory netnography” and “witness netnography,” which are derived from a mixed-methodology approach that integrates elements of netnography.

The findings of this study underscore the requisite methodological refinements imperative for enhancing netnographic analysis, particularly in its application for a better comprehension of individual behaviors within the realm of PBCs. In pursuit of this objective, the identified adjustments encompass ethical considerations, evaluation methods and their application in a digital milieu, where intricate mechanics and technologies frequently elude conventional methodologies.

In this study, the authors present a novel conceptualization of PBCs, highlighting their role and development, as well as the challenges they pose. To adequately capture the impact of PBC engagement overlap, the authors propose the need for an enriched mixed-methodological approach.

This chapter investigates the behavior of Reddit’s news subreddit users and the relationship between their sentiment on exchange rates. Using graphical models and natural language processing, hidden online communities among Reddit users are discovered. The data set used in this project is a mixture of text and categorical data from Reddit’s news subreddit. These data include the titles of the news pages, as well as a few user characteristics, in addition to users’ comments. This data set is an excellent resource to study user reaction to news since their comments are directly linked to the webpage contents. The model considered in this chapter is a hierarchical mixture model which is a generative model that detects overlapping networks using the sentiment from the user generated content. The advantage of this model is that the communities (or groups) are assumed to follow a Chinese restaurant process, and therefore it can automatically detect and cluster the communities. The hidden variables and the hyperparameters for this model are obtained using Gibbs sampling.

The purpose of this paper is to present an algorithm for detecting communities in social networks.

The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).

To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.

The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.

This chapter investigates the theoretical support for the distance metaphor that is widely used to capture the effects of institutional diversity in international business (IB) and management studies. It argues that neither new institutional economics (NIE) nor in neo-institutional sociology (NIS) offers support for a focus on the degree of dissimilarity. Rather, both literatures emphasize dis-commonality as a problem for cooperation. In the NIE argument, common enforcement mechanisms are needed to reduce transaction costs. In the NIS argument, effective communication and cooperation is limited to meaning-giving structures common to all parties. In neither perspective, the degree of difference in structures that are not common is relevant. We propose an alternative metaphor, institutional overlap, to capture the effects of institutional diversity on IB transactions. We argue that such a concept differs from institutional distance in being agency-centered, sensitive to intra-country variation, non-additive, and driving the thickness rather than the costs of transactions.

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.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

The author feels haunted; troubled by the ethnography that the author conducted some years ago of a new partnership group that was attempting to set up a community learning centre. The author is aware that it doesn’t sound like a particularly alarming research topic, and perhaps that is where some of the issues began. The author did not expect an ethnographic haunting to occur. The partnership recruited the author less than a year into the creation of the project and spent two years as a sort of ‘researcher in residence’. The original idea was that the author would observe the initial development of the project and then, when the community learning centre was established, the author would research the centre’s activities and how they were experienced by village residents. However, fairly soon into the project, problematic dynamics developed within the group, leading to irreconcilable conflict between members. The community learning centre was never established and the author was left to piece together an ethnography of a failed partnership. Researching an increasingly dysfunctional partnership was an emotionally exhausting activity, especially when relationships between members became progressively hostile. Managing data collection and analysis at this time was difficult, but the author was shocked that, a number of months (and now years) later, revisiting the data for publication purposes remained uncomfortable. The author managed to produce the PhD thesis on the back of this study, but the author has not felt able to go back to the data, despite there being findings worthy of publication. This ethnography is in a state of limbo and is at risk of becoming lost forever. In this chapter, the author explores the reasons for this and discusses lessons learned for future projects.

This paper aims to advance best practice by gaining insights into key multi-owned property (MOP) issues challenging policymakers and communities. Ontario (Canada) and Victoria (Australia) are internationally recognised for best practice in MOP living and law. Yet, both jurisdictions struggle with the emerging urbanism related to condominium MOP.

Different ways of recognising community in MOP urbanism will be examined against public policy and political theory perspectives promoting social sustainability. A rich mixed-data and content analysis method is relied upon which synthesises three pillars of MOP community governance: harmonious high-rise living; residential-neighbourhood interface; and metropolitan community engagement. The article cross-examines Canadian policy and law reform documents and Australian dispute case law from the state of Victoria to explore and showcase critical MOP management, residential and policy issues.

A theory-building typology formally recognises “community” as an affective performance across MOP governance contexts: cosmopolitan, civic-citizen and neighbourly. These ideal types differentiate community affects in and beyond (case) law and land-use planning: from determining alternative dispute resolution remedies; addressing neighbourhood and metropolitan NIMBY-ism in urban consolidation to bridging the critical policy and civic gap between the limits and aims of socially sustainable MOP vertical-tenured community affects.

Strong cross-jurisdictional MOP community lessons exist, as other cities follow best practice in legal and governance structures to effect change at the frontiers of twenty-first century urbanism.

Past studies emphasise classifying dispute issues, single-issue concerns or historical and life cycle evaluations. This theory-building article advances why and how community must be better understood holistically across community contexts to inform cutting-edge governance practices.

The purpose of this paper is to attempt to calculate the trust degree between two enterprises in an industrial network using grey correlation degree algorithm for exploring characteristics of community structure and evolution rules of cluster cooperation networks in axle‐type and satellite‐type clusters.

Starting from analysis of trust formation mechanism of inter‐enterprise in industrial networks, adjacency of inter‐enterprise relationship, their information acquisition ability, their influence power in network and their past interaction experience are chosen as influencing factors of the trust between two enterprises. Grey correlation degree algorithm was chosen to calculate the trust degree between two enterprises in an industrial network. According to the rules of dynamic adjustment of trust degree originated from thoughts of the prisoners' dilemma model, computer simulation is applied to explore characteristics of community structure and evolution rules of cluster cooperation network in axle‐type and satellite‐type clusters.

With the dynamic adjustment of enterprises' trust degree, the network density of axle‐type and satellite‐type cluster networks was decreasing as the cluster scale was enlarging, and eventually tended to be stable; community structure was emerged in axle‐type and satellite‐type industrial clusters as the cluster scale was enlarging; community characteristics were obviously stronger in axle‐type cluster networks than in satellite‐type; communities were overlapped in axle‐type cluster networks, that is, bridge nodes emerged between communities.

This paper is the first to apply the grey correlation degree algorithm to calculate the trust degree between two enterprises in cluster networks for designing the rules of dynamic adjustment of trust degree.