Fractal structures emerge from statistical and hierarchical processes in urban development or network evolution. In a class of efficient and robust geographical networks, we derive the size distribution of layered areas, and estimate the fractal dimension by using the distribution without huge computations. This method can be applied to self-similar tilings based on a stochastic process.

Recent studies investigating the Internet topology reported that inter Autonomous System (AS) topology exhibits a power-law degree distribution which is known as the scale-free property. Although there are many models to generate scale-free topologies, no game theoretic approaches have been proposed yet. In this paper, we propose the new dynamic game theoretic model for the AS level Internet topology formation. Through numerical simulations, we show our process tends to give emergence of the topologies which have the scale-free property especially in the case of large decay parameters and large random link costs. The significance of our study is summarized as following three topics. Firstly, we show that scale-free topologies can also emerge from the game theoretic model. Secondly, we propose the new dynamic process of the network formation game for modeling a process of AS topology formation, and show that our model is appropriate in the micro and macro senses. In the micro sense, our topology formation process is appropriate because this represents competitive and distributed situation observed in the real AS level Internet topology formation process. In the macro sense, some of statistical properties of emergent topologies from our process are similar to those of which also observed in the real AS level Internet topology. Finally, we demonstrate the numerical simulations of our process which is deterministic variation of dynamic process of network formation game with transfers. This is also the new result in the field of the game theory.

Low Power Design has become a significant requirement when the CMOS technology entered the nanometer era. Multiple-Supply Voltage (MSV) is a popular and effective method for both dynamic and static power reduction while maintaining performance. Level shifters may cause area and Interconnect Length Overhead (ILO), and should be considered at both floorplanning and post-floorplanning stages. In this paper, we propose a two phases algorithm framework, called VLSAF, to solve voltage and level shifter assignment problem. At floorplanning phase, we use a convex cost network flow algorithm to assign voltage and a minimum cost flow algorithm to handle level-shifter assignment. At post-floorplanning phase, a heuristic method is adopted to redistribute white spaces and calculate the positions and shapes of level shifters. The experimental results show VLSAF is effective.

In this paper, for systematically evaluating estimation methods of node characteristics, we first propose a social network generation model called LRE (Linkage with Relative Evaluation). LRE is a network generation model, which aims to reproduce the characteristics of a social network. LRE utilizes the fact that people generally build relationships with others based on relative evaluation, rather than absolute evaluation. We then extensively evaluate the accuracy of the estimation method called SSI (Structural Superiority Index). We reveal that SSI is effective for finding good nodes (e.g., top 10% nodes), but cannot be used for finding excellent nodes (e.g., top 1% nodes). For alleviating the problems of SSI, we propose a novel scheme for enhancing existing estimation methods called RENC (Recursive Estimation of Node Characteristic). RENC reduces the effect of noise by recursively estimating node characteristics. By investigating the estimation accuracy with RENC, we show that RENC is quite effective for improving the estimation accuracy in practical situations.

To understand the structural and functional properties of large-scale complex networks, it is crucial to efficiently extract a set of cohesive subnetworks as communities. There have been proposed several such community extraction methods in the literature, including the classical k-core decomposition method and, more recently, the k-clique based community extraction method. The k-core method, although computationally efficient, is often not powerful enough for uncovering a detailed community structure and it produces only coarse-grained and loosely connected communities. The k-clique method, on the other hand, can extract fine-grained and tightly connected communities but requires a substantial amount of computational load for large-scale complex networks. In this paper, we present a new notion of a subnetwork called k-dense, and propose an efficient algorithm for extracting k-dense communities. We applied our method to the three different types of networks assembled from real data, namely, from blog trackbacks, word associations and Wikipedia references, and demonstrated that the k-dense method could extract communities almost as efficiently as the k-core method, while the qualities of the extracted communities are comparable to those obtained by the k-clique method.

It has been widely observed that high-bandwidth traffic aggregates often occur by flooding-based distributed denial-of-service (DDoS) attacks. Several congestion control methods have been proposed for bandwidth controls. These methods are also considered to be important in order to avoid collapse of network services by DDoS attacks. We perform simulation studies of these well-known crowd management methods in order to minimize the damage caused by DDoS attacks with bandwidth control. Internet topologies have many facets in terms of the focus of the observation. Therefore, we need to conduct simulation of DDoS attacks in different Internet topologies, including the tiers model, the transit-stub model, and the Barabasi-Albert model. Using RED, CHOKe, and pushback with ACC as congestion control methods, we evaluate network resistance against DDoS attacks and similar overflow problems.

The relationship between the topology and collective function of a nonlinear oscillator network was investigated using nonlinear electrochemical oscillators. The constitutive experiments showed that the physiological robustness in the living system is due to their topological redundancy and asymmetry in the nonlinear network.