Classes of complex networks defined by role-to-role connectivity profiles

Interactions between units in phyical, biological, technological, and social systems usually give rise to intrincate networks with non-trivial structure, which critically affects the dynamics and properties of the system. The focus of most current research on complex networks is on global network properties. A caveat of this approach is that the relevance of global properties hinges on the premise that networks are homogeneous, whereas most real-world networks have a markedly modular structure. Here, we report that networks with different functions, including the Internet, metabolic, air transportation, and protein interaction networks, have distinct patterns of connections among nodes with different roles, and that, as a consequence, complex networks can be classified into two distinct functional classes based on their link type frequency. Importantly, we demonstrate that the above structural features cannot be captured by means of often studied global properties

Modes of magnetic resonance of S=1 dimer chain compound NTENP

The spin dynamics of a quasi one dimensional $S=1$ bond alternating spin-gap antiferromagnet Ni(C$_9$H$_{24}$N$_4$)NO$_2$(ClO$_4$) (abbreviated as NTENP) is studied by means of electron spin resonance (ESR) technique. Five modes of ESR transitions are observed and identified: transitions between singlet ground state and excited triplet states, three modes of transitions between spin sublevels of collective triplet states and antiferromagnetic resonance absorption in the field-induced antiferromagnetically ordered phase. Singlet-triplet and intra-triplet modes demonstrate a doublet structure which is due to two maxima in the density of magnon states in the low-frequency range. A joint analysis of the observed spectra and other experimental results allows to test the applicability of the fermionic and bosonic models. We conclude that the fermionic approach is more appropriate for the particular case of NTENP.Comment: 11 pages, 11 figures, published in Phys.Rev.

Modularity from Fluctuations in Random Graphs and Complex Networks

The mechanisms by which modularity emerges in complex networks are not well understood but recent reports have suggested that modularity may arise from evolutionary selection. We show that finding the modularity of a network is analogous to finding the ground-state energy of a spin system. Moreover, we demonstrate that, due to fluctuations, stochastic network models give rise to modular networks. Specifically, we show both numerically and analytically that random graphs and scale-free networks have modularity. We argue that this fact must be taken into consideration to define statistically-significant modularity in complex networks.Comment: 4 page