“…Generalization of clustering coefficients to graphs endowed with a weight function on edges has been considered by several authors (see for instance [18,31]). The process leading to the definition of γ v in (B.4) above extends naturally to such graphs, with similar connections to existing methods (see Theorem B.2, below).…”
Section: B2 Weighted Networkmentioning
confidence: 99%
“…note, for instance, the in-degree or out-degree of v in G, some other graph dependent measure such as betweenness or closeness (see for instance [31] and the references therein), or some attribute extraneous to G, itself, such as biomass in a food web.…”
Section: Introductionmentioning
confidence: 99%
“…For further recent work related to the friendship paradox, see for instance [12,20,24,31,15,33,22,18,32,36], and for further information on random walks on graphs, see for instance [26,1,25,7].…”
This paper studies the friendship paradox for weighted and directed networks, from a probabilistic perspective. We consolidate and extend recent results of Cao and Ross and Kramer, Cutler and Radcliffe, to weighted networks. Friendship paradox results for directed networks are given; connections to detailed balance are considered.
“…Generalization of clustering coefficients to graphs endowed with a weight function on edges has been considered by several authors (see for instance [18,31]). The process leading to the definition of γ v in (B.4) above extends naturally to such graphs, with similar connections to existing methods (see Theorem B.2, below).…”
Section: B2 Weighted Networkmentioning
confidence: 99%
“…note, for instance, the in-degree or out-degree of v in G, some other graph dependent measure such as betweenness or closeness (see for instance [31] and the references therein), or some attribute extraneous to G, itself, such as biomass in a food web.…”
Section: Introductionmentioning
confidence: 99%
“…For further recent work related to the friendship paradox, see for instance [12,20,24,31,15,33,22,18,32,36], and for further information on random walks on graphs, see for instance [26,1,25,7].…”
This paper studies the friendship paradox for weighted and directed networks, from a probabilistic perspective. We consolidate and extend recent results of Cao and Ross and Kramer, Cutler and Radcliffe, to weighted networks. Friendship paradox results for directed networks are given; connections to detailed balance are considered.
“…Several studies have explored weighted network analyses (Barrat et al 2004; Phan Binh and Fjeldstad Øystein 2013; Tore Opsahl 2009) and defined open triplets, which are composed of two edges, and closed triplets, which are composed of three edges. Additionally, a triangle is defined as containing three closed triplets.…”
Section: Related Workmentioning
confidence: 99%
“…One type of method (i.e., the SCAN method) uses the composition of the structure of an entire network (Xu et al 2007). The other type of method only considers the weights of the edges (Barrat et al 2004; Phan Binh and Fjeldstad Øystein 2013; Tore Opsahl 2009). We believe that both factors are important to cluster friends; thus, the proposed approach in this study combines these two concepts.…”
Section: Mcaf: Multi-dimensional Clustering Algorithm For Friendsmentioning
In recent years, social network services have grown rapidly. The number of friends of each user using social network services has also increased significantly and is so large that clustering and managing these friends has become difficult. In this paper, we propose an algorithm called mCAF that automatically clusters friends. Additionally, we propose methods that define the distance between different friends based on different sets of measurements. Our proposed mCAF algorithm attempts to reduce the effort and time required for users to manage their friends in social network services. The proposed algorithm could be more flexible and convenient by implementing different privacy settings for different groups of friends. According to our experimental results, we find that the improved ratios between mCAF and SCAN are 35.8 % in similarity and 84.9 % in F1 score.
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