Triadic closure in two-mode networks: Redefining the global and local clustering coefficients

By Tore Opsahl. Soc. Networks, 2010

541 citations recorded by the source corpus.

Abstract As the vast majority of network measures are defined for one-mode networks, two-mode networks often have to be projected onto one-mode networks to be analyzed. A number of issues arise in this transformation process, especially when analyzing ties among nodes’ contacts. For example, the values attained by the global and local clustering coefficients on projected random two-mode networks deviate from the expected values in corresponding classical one-mode networks. Moreover, both the local clustering coefficient and constraint (structural holes) are inversely associated to nodes’ two-mode degree. To overcome these issues, this paper proposes redefinitions of the clustering coefficients for two-mode networks.

Subfields: Computer Networks and Communications, Geometry and Topology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/6ee9809509751e426bd295ac16a5b03b9f7b95ef

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In Computer Networks and Communications

In Geometry and Topology

In Statistical and Nonlinear Physics

Citations in the corpus

Linear Algebra and its Applications (2010, unknown): “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).”
The Geography of Complex Knowledge (2017, background): “This type of network is also referred to as a bipartite, bimodal, or an affiliation network in the network science literature (Opsahl 2013).”
Simplicial closure and higher-order link prediction (2018, background): “Prior research has also identified the distinction between open and closed triangles when projecting bipartite networks but has not studied the idea of simplicial closure events (7, 48).”
Representation Learning for Dynamic Graphs: A Survey (2019, background): “While the adjacency matrix of a graph only represents local proximities, one can also define high-order proximity matrices (Ou et al. (2016)) also known as graph similarity metrics (da Silva Soares and Prudêncio (2012)).”
Networks beyond pairwise interactions: structure and dynamics (2020, background): “The global clustering coefficient can however be defined through its one-mode projections, as the number of 4-paths in the bipartite graph that are part of a 6-cycle [112, 113].”
The Role of External Linkages and Gatekeepers for the Renewal and Expansion of US Cities' Knowledge Base, 1990–2004 (2015, methodology): “To correct for this possible bias and inflationary mechanism, the generalized version of the clustering coefficient proposed by OPSAHL (2013) was computed, which excludes those triples of inventors connected because of a joint patent and only counts the number of triples (i.e. closed paths of…”