Bose-Einstein condensation in complex networks.

By Ginestra Bianconi, Albert-László Barabási. Physical Review Letters, 2000

637 citations recorded by the source corpus.

The evolution of many complex systems, including the World Wide Web, business, and citation networks, is encoded in the dynamic web describing the interactions between the system's constituents. Despite their irreversible and nonequilibrium nature these networks follow Bose statistics and can undergo Bose-Einstein condensation. Addressing the dynamical properties of these nonequilibrium systems within the framework of equilibrium quantum gases predicts that the "first-mover-advantage," "fit-get-rich," and "winner-takes-all" phenomena observed in competitive systems are thermodynamically distinct phases of the underlying evolving networks.

Subfields: Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/752b933a6d9e821d38b0502b34876db27f28655c

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Citations in the corpus

The science of science: from the perspective of complex systems (2017, background): “In some literature, the obtained ⌘i is interpreted as a paper’s intrinsic quality [125].”
Evolution of networks (2001, background): “m+ n, since a more rapid growth of q ˜s(t) is impossible in principle. This means that, for g>gc, a ﬁnite fraction of all preferentially distributed edges is captured by the strong vertex (in Ref. [184] just this situation is called the Bose-Einstein condensation). We see that a single strong ver”
The Structure and Function of Complex Networks (2003, methodology): “Then in Section VII we look at models of growing networks, particularly the models of Price [344] and Barabási and Albert [32], and generalizations.”
Basis for a neuronal version of Grover's quantum algorithm (2014, background): “One may draw direct comparison of these sorts of effects with the operation of closely related technological quantum networks performing search functions (e.g., Bianconi and Barabási, 2001; Bianconi, 2002a,b, 2003; Stella et al., 2005; Clark, 2010b,c,d, 2011, 2012a,b, 2013a).”
Popularity versus similarity in growing networks (2011, methodology): “In this section we consider the popular ﬁtness model [10] and show that it can be also mapped to our geometric optimization framework.”
Analyzing and modeling real-world phenomena with complex networks: a survey of applications (2007, background): “For instance, in 2001, Bianconi and Barabási [500] discovered that a Bose-Einstein condensate was related to complex networks.”
Random Graphs and Complex Networks (2016, background): “Models that can be treated within this general methodology include fitness models [41, 42, 114], competition-induced 2The notion of degree used in [222] is slightly different since [222] makes use of the in-degree only.”
Neural Field Continuum Limits and the Structure–Function Partitioning of Cognitive–Emotional Brain Networks (2023, background): “ments [81,82,87,98,99] to permit nodes to be revisited during cognitive-emotional processing without violating Equation (1).”
Statistical mechanics of complex networks (2001, background): “Bianconi and Barabási (2001a) argue that real networks have a competitive aspect, as each node has an intrinsic ability to compete for edges at the expense of other nodes.”