Abstract:
We examined the effects of inhomogeneity on the dynamics and structural properties using Boolean networks. Two different power-law rank outdegree distributions were embedded to determine the role of hubs. The degree of randomness and coherence of the binary sequence in the networks were measured by entropy and mutual information, depending on the number of outdegrees and types of Boolean functions for the hub. With a large number of outdegrees, the path length from the hub reduces as well as the effects of Boolean function on the hub are more prominent. These results indicate that the hubs play important roles in networks' dynamics and structural properties. By comparing the effect of the skewness of the two different power-law rank distributions, we found that networks with more uniform distribution exhibit shorter average path length and higher event probability of coherence but lower degree of coherence. Networks with more skewed rank distribution have complementary properties. These results indicate that highly connected hubs provide an effective route for propagating their signals to the entire network.

Abstract:
We propose a model for evolving networks by merging building blocks represented as complete graphs, reminiscent of modules in biological system or communities in sociology. The model shows power-law degree distributions, power-law clustering spectra and high average clustering coefficients independent of network size. The analytical solutions indicate that a degree exponent is determined by the ratio of the number of merging nodes to that of all nodes in the blocks, demonstrating that the exponent is tunable, and are also applicable when the blocks are classical networks such as Erd\H{o}s-R\'enyi or regular graphs. Our model becomes the same model as the Barab\'asi-Albert model under a specific condition.

Abstract:
We propose a growing network model that consists of two tunable mechanisms: growth by merging modules which are represented as complete graphs and a fitness-driven preferential attachment. Our model exhibits the three prominent statistical properties are widely shared in real biological networks, for example gene regulatory, protein-protein interaction, and metabolic networks. They retain three power law relationships, such as the power laws of degree distribution, clustering spectrum, and degree-degree correlation corresponding to scale-free connectivity, hierarchical modularity, and disassortativity, respectively. After making comparisons of these properties between model networks and biological networks, we confirmed that our model has inference potential for evolutionary processes of biological networks.

Abstract:
We demonstrate the advantages of feedforward loops using a Boolean network, which is one of the discrete dynamical models for transcriptional regulatory networks. After comparing the dynamical behaviors of network embedded feedback and feedforward loops, we found that feedforward loops can provide higher temporal order (coherence) with lower entropy (randomness) in a temporal program of gene expression. In addition, complexity of the state space that increases with longer length of attractors and greater number of attractors is also reduced for networks with more feedforward loops. Feedback loops show opposite effects on dynamics of the networks. These results suggest that feedforward loops are one of the favorable local structures in biomolecular and neuronal networks.

Abstract:
We propose the n-clique network as a powerful tool for understanding global structures of combined highly-interconnected subgraphs, and provide theoretical predictions for statistical properties of the n-clique networks embedded in a complex network using the degree distribution and the clustering spectrum. Furthermore, using our theoretical predictions, we find that the statistical properties are invariant between 3-clique networks and original networks for several observable real-world networks with the scale-free connectivity and the hierarchical modularity. The result implies that structural properties are identical between the 3-clique networks and the original networks.

Abstract:
We performed a numerical study on random Boolean networks with power-law rank outdegree distributions to find local structural cause for the emergence of high or low degree of coherence in binary state variables. The degree of randomness and coherence of the binary sequence are measured by entropy and mutual information, depending on local structure that consists of a node with a highly connected, called hub and its upstream nodes, and types of Boolean functions for the nodes. With a large number of output connections from a hub, the effects of Boolean function on the hub are more prominent. The local structures that give larger entropy tend to give rise to larger mutual information. Based on the numerical results and structural conditions we derived a time-independent transmission characteristic function of state variables for the local structures. We obtained good relationships between the numerical and analytical results, which indicate that dynamical properties from the whole networks can be inferred from the differences in the local structures.

Abstract:
We demonstrate the effects of embedding subgraphs using a Boolean network, which is one of the discrete dynamical models for transcriptional regulatory networks. After comparing the dynamical properties of network embedded seven different subgraphs including feedback and feedforward subgraphs, we found that complexity of the state space that increases with longer length of attractors and greater number of attractors is reduced for networks with more feedforward subgraphs. In addition, feedforward subgraphs can also provide higher mutual information with lower entropy in a temporal program of gene expression. Networks with other six subgraphs show opposite effects on dynamics of the networks, is roughly consistent with Thomas's conjecture. These results suggest that feedforward subgraphs are one of the favorable local structures in biological complex networks.

Abstract:
The effect of exchange bond randomness on the ground state and the field-induced magnetic ordering was investigated through magnetization measurements in the spin-1/2 mixed quantum spin system (Tl$_{1-x}$K$_{x}$)CuCl$_3$ for $x<0.36$. Both parent compounds TlCuCl$_3$ and KCuCl$_3$ are coupled spin dimer systems, which have the singlet ground state with excitation gaps ${\Delta}/k_{\rm B}=7.7$ K and 31 K, respectively. Due to bond randomness, the singlet ground state turns into the magnetic state with finite susceptibility, nevertheless, the excitation gap remains. Field-induced magnetic ordering, which can be described by the Bose condensation of excited triplets, magnons, was observed as in the parent systems. The phase transition temperature is suppressed by the bond randomness. This behavior may be attributed to the localization effect.

Abstract:
Specific heat measurements have been performed in the coupled dimer system TlCuCl$_3$, which has a singlet ground state with the excitation gap $\Delta \simeq 7.5$K. The cusplike anomaly indicative of the 3D magnetic ordering was clearly observed in magnetic fields higher than the critical field $H_c$ corresponding to the gap $\Delta$. The phase boundary determined by the present specific heat measurements coincides with that determined by previous magnetization measurements. The phase boundary can be described by the power law $[ H_{\rm c}(T)-H_{\rm g} ] \propto T^{\phi}$ with $\phi=2.1(1)$. This result supports the magnon Bose condensation picture for the field-induced magnetic ordering in TlCuCl$_3$ [Nikuni {\it et al}., Phys. Rev. Lett. {\bf 84} (2000) 5868].

Abstract:
The magnetization measurements have been performed on the doped spin gap system TlCu_{1-x}Mg_xCl_3 with x <= 0.025. The parent compound TlCuCl_3 is a three-dimensional coupled spin dimer system with the excitation gap Delta/k_B = 7.7 K. The impurity-induced antiferromagnetic ordering was clearly observed. The easy axis lies in the (0,1,0) plane. It was found that the transition temperature increases with increasing Mg^{2+} concentration x, while the spin-flop transition field is almost independent of x. The magnetization curve suggests that the impurity-induced antiferromagnetic ordering coexists with the spin gap for x <= 0.017.