We suggest an underlying mechanism that governs the growth of a network of concepts, a complex network that reflects the connections between different scientific concepts based on their co-occurrences in publications. To this end, we perform empirica
l analysis of a network of concepts based on the preprints in physics submitted to the arXiv.org. We calculate the network characteristics and show that they cannot follow as a result of several simple commonly used network growth models. In turn, we suggest that a simultaneous account of two factors, i.e., growth by blocks and preferential selection, gives an explanation of empirically observed properties of the concepts network. Moreover, the observed structure emerges as a synergistic effect of these both factors: each of them alone does not lead to a satisfactory picture.
The Ising model on annealed complex networks with degree distribution decaying algebraically as $p(K)sim K^{-lambda}$ has a second-order phase transition at finite temperature if $lambda> 3$. In the absence of space dimensionality, $lambda$ controls
the transition strength; mean-field theory applies for $lambda >5$ but critical exponents are $lambda$-dependent if $lambda < 5$. Here we show that, as for regular lattices, the celebrated Lee-Yang circle theorem is obeyed for the former case. However, unlike on regular lattices where it is independent of dimensionality, the circle theorem fails on complex networks when $lambda < 5$. We discuss the importance of this result for both theory and experiments on phase transitions and critical phenomena. We also investigate the finite-size scaling of Lee-Yang zeros in both regimes as well as the multiplicative logarithmic corrections which occur at $lambda=5$.
The Potts model is one of the most popular spin models of statistical physics. The prevailing majority of work done so far corresponds to the lattice version of the model. However, many natural or man-made systems are much better described by the top
ology of a network. We consider the q-state Potts model on an uncorrelated scale-free network for which the node-degree distribution manifests a power-law decay governed by the exponent lambda. We work within the mean-field approximation, since for systems on random uncorrelated scale-free networks this method is known to often give asymptotically exact results. Depending on particular values of q and lambda one observes either a first-order or a second-order phase transition or the system is ordered at any finite temperature. In a case study, we consider the limit q=1 (percolation) and find a correspondence between the magnetic exponents and those describing percolation on a scale-free network. Interestingly, logarithmic corrections to scaling appear at lambda=4 in this case.
