Scale-Free Networks
By Guido Caldarelli. 2007
572 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/d72f7ad1d80eabf7fda422721e2bb466a018a7b1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Tore Opsahl
- Masashi Sugiyama
- Hisashi Kashima
- Dominick Lee
- Chris Cooper
- Ernesto Estrada
- Miguel Angel Muñoz
- Jukka-Pekka Onnela
Citations in the corpus
- Communities in Networks (2009, background): “ticalMechanics, 1902 [40] From an abstract perspective, the term network is used as a synonym for a mathematical graph. However, to scientists across a variety of fields, this label means so much more [11,17,37,74,79,108,112]. In sociology, each node(or vertex) of a network represents an agent, and a”
- Knowledge transfer in a tourism destination: the effects of a network structure (2009, background): “The mathematical expressions for these quantities can be found in one of the recently published reviews of the research in this area (Caldarelli, 2007; da Fontoura Costa, Rodrigues, Travieso, & Villas Boas, 2007).”
- Comparing Community Structure to Characteristics in Online Collegiate Social Networks (2008, background): “The global organization of real-world networks typically includes coexisting modular (horizontal) and hierarchical (vertical) organizational structures [5, 8, 28, 30, 33].”
- Geographic Constraints on Social Network Groups (2010, background): “d text messages into a symmetric unweighted network, eectively combining the two modes of interaction. We then proceeded to maximize modularity dened as Q= 1 2w X i;j A ij k ik j 2w (c i;c j); (2) where the adjacency matrix element A ij denotes the presence (A ij = 1) or absence (A ij = 0) of a co”
- A Transfer Learning Approach and Selective Integration of Multiple Types of Assays for Biological Network Inference (2010, background): “One shortcoming of their method is that the amount of local information is often limited, because most of the nodes have a few edges due to the fact that the node connectivity in molecular networks follows a power law distribution (Caldarelli, 2007).”
- Controlling edge dynamics in complex networks (2011, unknown): “For power-law distributed digraphs [8, 13] with P(dv = k) = P(d − v = k) = Ck −γe−k/κ, nD is given by n D = 1 2 − Li2γ(e −2/κ) 2 Liγ(e−1/κ)2 (7)”
- The Physics of Communicability in Complex Networks (2011, background): “Complex networks are also ubiquitous in many biological, ecological, technological, informational, and infrastructural systems (Albert and Barabási, 2002; Barabási and Oltvai, 2003; Caldarelli, 2007; da Fontoura Costa et al., 2011; Dorogovtsev and Mendes, 2003; Newman, 2003; 2010; Strogatz, 2001; Wa”