Clustering in weighted networks

By Tore Opsahl, Pietro Panzarasa. Soc. Networks, 2009

1,052 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/95f568b1dfc1078b6a6227f522c5406b2d52e949

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In Artificial Intelligence

In Statistical and Nonlinear Physics

Citations in the corpus

Validity Issues in the Use of Social Network Analysis with Digital Trace Data (2011, neutral): “Consult Opsahl and Panzarasa (2009) and the TNET R package (http://opsahl.co.uk/ tnet/) for measures that utilize intensity.”
PRISM: concept-preserving social image search results summarization (2014, methodology): “…the visual cohesiveness score of a summary, denoted by VCS , as follows: where w is the visual similarity weight function of C T , the numerator (cid:4) T Δ (cid:4) e ∈ T Δ w ( e ) sums over all closed triplets T Δ in C T , and (cid:4) T (cid:4) e ∈ T w ( e ) sums over all triplets T in C T [11].”
Study on centrality measures in social networks: a survey (2018, background): “(2004), Newman (2004) and Opsahl and Panzarasa (2009) generalized and extended the degree of node by considering sum of weights in place of number ties.”
Node centrality in weighted networks: Generalizing degree and shortest paths (2010, methodology): “There have been several attempts to identify shortest paths in weighted networks (Dijkstra, 1959; Katz, 1953; Peay, 1980; Yang and Knoke, 2001). Dijkstra (1959) proposed an algorithm that finds the path of least resistance, and was defined for networks where the weights represented costs of transmit”
Large-Scale Embedding Learning in Heterogeneous Event Data (2016, background): “The lowdimensional vectors, as distributed representations of objects, are beneficial for various downstream applications, such as exploratory data analysis, link prediction [1], object clustering [2], classification [3], and recommendation [4].”
Triadic closure in two-mode networks: Redefining the global and local clustering coefficients (2010, methodology): “ee nodes solely being connected to the same other nodes (e.g., three scientists writing a paper). For each of the proposed coefficients, I will suggest a method for integrating the generalizations by Opsahl and Panzarasa (2009) and Barrat et al. (2004) of the clustering coefficients to weighted one-”
Linear Algebra and its Applications (2010, neutral): “For formulating our exponential integrator we rewrite the semi-discrete system (1.2) as where J = M − 1 A and f ( t ) = M − 1 g ( t ) and w , M , A and g ( t ) are deﬁned as in (3.4).”
A Network Analysis of Global Banking:1978-2009 (2011, positive): “Our weighted clustering coefficient is inspired by the generalized node clustering coefficient proposed by Opsahl and Panzarasa (2009), which counts complete triangles as defined above and calculates their total value as the geometric mean of their weights (i.”
Voice Flows to and around Leaders (2013, methodology): “Consonant with prior network research (e.g., Wasserman and Faust, 1994; Opsahl and Panzarasa, 2009), we first dichotomized the responses such that scores indicating any level of voice (seldom or more frequently) were coded as an existing voice tie, and ‘‘no’’/ ‘‘never’’ responses were coded as lack…”
Learning Latent Block Structure in Weighted Networks (2014, background): “hared members [30]. Forum. Vertices represent n= 1899 users of a student social network at UC Irvine, and each of the m= 20291 directed edges is weighted by the number of messages sent between users [24]. College FB. Vertices represent the n= 1411 NCAA college football teams, and each of the m= 2216”
Exploring spatio-temporal changes of city inbound tourism flow: The case of Shanghai, China (2020, background): “The formula is (Opsahl & Panzarasa, 2009): The clustering coefficient of the node indicates the tightness of the interconnection between the nodes in the network.”
State of the aRt personality research: A tutorial on network analysis of personality data in R (2015, positive): “Transitivity has been extended Running Head: STATE OF THE ART PERSONALITY REASERCH by Opsahl and Panzarasa (2009) to take into account edge weights and directions, and by Kunegis and collaborators to signed networks (Kunegis et al., 2009).”
GOTCHA! Network-Based Fraud Detection for Social Security Fraud (2017, neutral): “The value of a weighted quadrangle is determined by the arithmetic mean of the link weights (Opsahl and Panzarasa 2009).”