The Laplacian anomaly detection (LAD) has been proposed as an innovative method for detecting anomalies in dynamic networks. LAD calculates the anomaly score, i.e., an indicator of the degree of deviation from the normal state, at each time from timeseries data for some eigenvalues of the Laplacian matrix, which represents the structure of networks. The original LAD uses only the large eigenvalues of the Laplacian matrix; other eigenvalue combinations have not been discussed for anomaly detection. In this paper, through preliminary evaluation, we investigate the impact of small eigenvalues of the Laplacian matrix on the anomaly detection of dynamic networks.

The first meeting time is defined by the time it takes for multiple mobile agents starting random walks from different nodes on a graph to first meet at the same node. Understanding the characteristics of the first meeting time is important to design an efficient rendezvous algorithm on a graph. In the previous work, we analyzed two mobile agents performing simple random walks with the same transition probability, and derived the expected value of the first meeting time. In this paper, we derive the spectral formula for the expected first meeting time of diverse random walk agents with different transition probabilities and movement frequencies.

A blind search in a network is used to discover a target node without detailed knowledge on the network. Because of its simplicity and the robust against network uncertainty, the blind search has been widely utilized by diverse applications in different types of networks (e.g., unstructured P2P (Peer-to-Peer) networks, ICNs (Information Centric Networks), mobile ad-hoc networks, and social networks). One of the major drawbacks of the blind search is its inefficiency; i.e., a large number of message exchanges is unavoidable for shortening the search time. In this paper, we propose an efficient blind search method utilizing the rendezvous of multiple random walkers, whose transition probabilities are adjusted based on our analysis results. Through simulation experiments, we show that the performance of the proposed search method is comparable with the flooding, which is the fastest but the least efficient method among blind search methods, and that it requires much smaller message exchanges than the flooding. We also show that the proposed search method works more effectively in scale-free networks than in non-scale-free networks.

We model the behavior of a mobile agent as a random walk on a network, and derive the formula of the expected time until two random walkers meet on the basis of the spectral graph theory. The validity of the derived formula is confirmed by the comparison with simulation results. We believe that our work contributes to understanding the property of epidemic information dissemination, and designing a mechanism for efficient dissemination in mobile ad hoc networks.

Renewable energy generation has unstable output, so is hard to use independently. In order to improve renewable energy's ease of use, we have proposed the MCMC-based autonomous decentralized mechanism (ADM) to perform energy interchange between AESs so as to be supply energy appropriately for different energy demands among AESs. In this paper, toward the practical realization of our ADM, we investigate its performance in a practical scenario with the fluctuation in energy generation. We conduct the experiment with a model based on the characteristic of energy generation fluctuation with a PV panel, and clarify the effectiveness of our ADM against fluctuation.

People interaction on social media has a significant impact on public opinion formation. Disagreement, which is a kind of people interaction, causes dissensus among persons, and makes it difficult to build consensus in the society. Many studies investigated social networks with disagreement relationships. In particular, one study discussed the opinion formulation with a decision-making model where two persons with a disagreement relationship hold an opposite opinion each other. However, there are more than two opinions in actual situations. In such situations, the opinions of the two persons are not limited to only opposite ones, and they can hold a distant opinion each other in dissensus. In this paper, we analyze a decision-making model with a relaxed dissensus condition of disagreement relationship, and derive the condition of social networks where all the persons can be satisfied under the decision-making model. According to the derived condition, such satisfiable social networks have no cycles with one negative link. Moreover, through simulation experiment, we clarify that persons are categorized into multiple fractions in satisfiable social networks. We believe that such categorization of persons helps consensus building in a society, and opens new social media for the betterment of society.

It is not easy to provide energy supply based on renewable energy enough to satisfy energy demand anytime and anywhere because renewable energy amounts depends on geographical conditions and the time of day. This paper proposes a novel autonomous decentralized mechanism of energy interchanges between distributed batteries on the basis of the diffusion equation and MCMC (Markov chain Monte Carlo) for realizing energy supply appropriately for energy demand. Experimental results show the proposed mechanism effectively works under several situations.

We have proposed an autonomous decentralized control using a local action rule for indirectly controlling the probability distribution of a system performance variable on the basis of markov chain monte carlo, while not measuring the variable. In this paper, we design an autonomous decentralized adaptive function for retaining the control strength of our control under a changing environment as an example of global controls appropriately reflecting information of external environment. We apply our control with the adaptive function to a virtual machine placement problem in a Data Center Network (DCN). Through simulation experiments, we confirm that the adaptive function effectively deals with several scenarios with a changing environment in a DCN.

XCP (eXplicit control protocol) has been proposed as an efficient transport protocol for a wide-area and high-speed network. XCP is a transport-layer protocol that performs congestion control using explicit feedback from routers. In the literature, many simulation-based performance studies of XCP has been performed. However, the effect of traffic dynamics on the XCP performance has not been investigated. In this paper, through simulation experiments, we first show that XCP has the following problems: (1) utilization of the bottleneck link is lowered due to XCP traffic dynamics, and (2) in environment where non-XCP traffic and XCP traffic coexist, control of XCP becomes unstable. We then propose XCP-IR (XCP with increased robustness) that operates efficiently even for dynamic traffic. Through simulation experiments, we show that XCP-IR operates efficiently even for dynamic traffic.

In this paper, we analyze stability of XCP (explicit control protocol) in a network with heterogeneous XCP flows (i.e., XCP flows with different propagation delays). Specifically, we model a network with heterogeneous XCP flows using fluid-flow approximation. We then derive the conditions that XCP control parameters should satisfy for stable XCP operation. Furthermore, through several numerical examples and simulation results, we quantitatively investigate effect of system parameters and XCP control parameters on stability of the XCP protocol. Our findings include: (1) when XCP flows are heterogeneous, XCP operates more stably than the case when XCP flows are homogeneous, (2) conversely, when variation in propagation delays of XCP flows are very large, operation of XCP becomes less stable, and (3) output link bandwidth of an XCP router is independent of stability of the XCP protocol.

In this paper, we propose a flow-level simulator called FSIM (Fluid-based SIMulator) for performance evaluation of large-scale networks, and verify its effectiveness using our FSIM implementation. The notable features of our flow-level simulator FSIM are its accuracy and fast simulation execution compared with conventional flow-level simulators. For improving simulation accuracy, our flow-level simulator FSIM utilizes accurate fluid-flow models. For accelerating simulation execution speed, our flow-level simulator FSIM adopts an adaptive numerical computation algorithm for ordinary differential equations. Another notable feature of our flow-level simulator FSIM is its compatibility with the existing network performance analysis tool. In this paper, through several experiments using our FSIM implementation, we evaluate the effectiveness of our flow-level simulator FSIM in terms of simulation speed, accuracy and memory consumption. Consequently, we show that our flow-level simulator FSIM outperforms a conventional flow-level simulator; i.e., it realizes approximately 100% faster simulation with higher accuracy and less memory consumption than a conventional flow-level simulator.