Highly clustered scale-free networks.

By K. Klemm, Víctor M. Eguíluz. Physical review. E, Statistical, nonlinear, and soft matter physics, 2001

351 citations recorded by the source corpus.

We propose a model for growing networks based on a finite memory of the nodes. The model shows stylized features of real-world networks: power-law distribution of degree, linear preferential attachment of new links, and a negative correlation between the age of a node and its link attachment rate. Notably, the degree distribution is conserved even though only the most recently grown part of the network is considered. As the network grows, the clustering reaches an asymptotic value larger than that for regular lattices of the same average connectivity and similar to the one observed in the networks of movie actors, coauthorship in science, and word synonyms. These highly clustered scale-free networks indicate that memory effects are crucial for a correct description of the dynamics of growing networks.

Subfields: Condensed Matter Physics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/d21ca495eadcd95bb4562cbdb897946733f925d8

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In Condensed Matter Physics

In Statistical and Nonlinear Physics

Citations in the corpus

The science of science: from the perspective of complex systems (2017, background): “Empirical studies reveal that feed-forward loops are abundant in citation networks [111]; however, the BA model and its variants mentioned above cannot capture this feature.”
Discriminative topological features reveal biological network mechanisms (2004, background): “[30], [31] Small World Interpolation between a regular lattice and a random graph.”
The Structure and Function of Complex Networks (2003, background): “28 For example, a number of authors [30, 102, 197, 216, 219, 241, 396, 397, 410, 411] have studied models of network transitivity that make use of “triadic closure” processes.”
Kronecker Graphs: An Approach to Modeling Networks (2008, result): “And, contrary to other models that match this combination of network properties (as for example, Bu and Towsley, 2002; Klemm and Eguı́luz, 2002; Vázquez, 2003; Leskovec et al., 2005b; Zheleva et al., 2009), Kronecker graphs also lead to tractable analysis and rigorous proofs.”
Deciphering microbial interactions and detecting keystone species with co-occurrence networks (2014, methodology): “…networks with small world property, (iii) the Barabasi-Albert model (Barabasi and Albert, 1999) generating scale-free networks, and (iv) the Klemm-Eguiluz model (Klemm and Eguíluz, 2002; Prettejohn et al., 2011) generating topologies with small world and scale-free properties as well as modularity.”
Why social networks are different from other types of networks. (2003, neutral): “Several models of this “triadic closure” process have been studied in the literature [35, 36, 37, 38, 39, 40].”
The Economics of Social Networks (2005, background): “In order to generate clustering, and still have some sort of scale-free aspect to a degree distribution, Klemm and Eguíluz (2002, 2002b) have a variation of the preferential attachment model where nodes are declared either active or inactive.”
Popularity versus similarity in growing networks (2011, background): “One such mechanism is node activation/deactivation [66] motivated by citation networks.”