Detecting large cohesive subgroups with high clustering coefficients in social networks

A Review of the Research Progress of Social Network Structure

“…Studies have shown that in real networks, due to the relatively high density of connection points, nodes always tend to establish a set of tight organizational relationships, which is often more likely than a random connection between two nodes. e clustering coefficient includes the global clustering coefficient, local clustering coefficient, and average clustering coefficient [58], where the global clustering coefficient is based on node triples and calculated by the number of closed triples/the number of all triples. e local clustering coefficient of a node indicates the degree of compactness of its adjacent nodes to form a cluster.…”

Vulnerability of clustering under node failure in complex networks

“…Our work overcomes this particular shortcoming as ALCC is a well-defined and commonly accepted concept for quantifying the clustering of a network. Ertem et al (2016) studied the problem of how to detect groups of nodes in a social network with high clustering coefficient; however, their work does not consider the vulnerability of the average clustering coefficient of a network. The diffusion of information in a social network has been studied from many perspectives, including worm containment (Nguyen et al, 2010), viral marketing (Dinh et al, 2012aKempe et al, 2003;Kuhnle et al, 2017), and the detection of overlapping communities (Nguyen et al, 2011).…”

A Retrospective Clinical Study to Evaluate Treatment Outcomes of Vital Pulp Therapy with ProRootRTM Mineral Trioxide Aggregate, EndosequenceRTM Root Repair Material, and Biodentine RTM

“…These groups of strong ties, also referred to as clusters (Granovetter, 1983), form cliques and interest groups, often referred to as communities (Granovetter, 1983;Girvan & Newman, 2002;Newman, 2003). A characteristic of clusters is their density, or the number of direct connections among the individuals in the cluster (Ertem, Veremyev, & Butenko, 2016;Newman, 2003;Watts & Strogatz, 1998). For instance, a densely-knit cluster that consists of a group of individuals (three or more people) who all share relationships, or direct ties, with each other, is a clique (Luce & Perry, 1949) and a cluster with a small number of direct ties is considered relaxed (Ertem et al, 2016).…”

“…A characteristic of clusters is their density, or the number of direct connections among the individuals in the cluster (Ertem, Veremyev, & Butenko, 2016;Newman, 2003;Watts & Strogatz, 1998). For instance, a densely-knit cluster that consists of a group of individuals (three or more people) who all share relationships, or direct ties, with each other, is a clique (Luce & Perry, 1949) and a cluster with a small number of direct ties is considered relaxed (Ertem et al, 2016). For instance, an individual's friends from school may not all know each other, forming a relaxed cluster, whereas an individual's sports team or immediate family can be considered a clique because, everyone is connected to each other.…”

“…In other words, a user may feel as if their network is aggressive and strict because of how salient and reinforced that specific social context's norms are. Alternately, if the cluster is relaxed (i.e., a group which is only loosely knit together; Ertem et al, 2016) and only some of the individuals are connected or all group members are only weakly connected, then each strong tie may have its own set of injunctive norms set in place from the clique they are associated with. This collision of injunctive norms could also result in increased feelings of evaluation apprehension associated with having to conform to the norms of multiple cliques.…”