The excessive activation of user dynamics, such as online flaming, causes various social issues, making effective intervention based on early detection desirable. Current early detection methods identify increased user activity based on quantitative changes in time-series data, such as whether the number of social media posts exceeds a threshold. However, from a theoretical standpoint rooted in fundamental principles, it is expected that the precursor to excessive activation of user dynamics due to structural changes in social networks will manifest itself as the emergence of a low-frequency mode in the time series of user dynamics intensity. This research describes a method for the early detection of excessive activation of user dynamics by identifying the emergence of low-frequency modes through frequency spectrum analysis of actual SNS data, a method faster than the quantitative observation of time-series data.

The recent surge in traffic demand has accelerated the development of heterogeneous high-density networks. Consequently, the number of base stations has increased, leading to a rise in the frequency of vertical handovers between heterogeneous base stations, which results in delays and increased power wastage. Earlier studies analyzed the number of vertical handovers in such networks. Unfortunately, while the number of vertical handovers with short sojourn periods is increasing, the sojourn time itself has not been well addressed. This paper prepare to analyze vertical handover with direct consideration of the short cell sojourn time distribution as derived from stochastic geometry. Additionally, by referencing Bertrand’s paradox, we define and simulate randomness in three different scenarios to validate our findings.

Summary The oscillation model, based on the wave equation on networks, can describe user dynamics in online social networks. The fundamental equation of user dynamics can be introduced into the oscillation model to explicitly describe the causal relation of user dynamics yielded by certain specific network structures. Moreover, by considering the sparseness of the link structure of online social networks, a novel fundamental equation of different forms has been devised. In this article, we derive a closed‐form solution of the new fundamental equation. Also, we show that the closed‐form solution of the new fundamental equation can generate the general solution of the original wave equation and investigate the characteristics of the derived general solution.

Online social networks (OSNs) are threatened by Sybil attacks, which create fake accounts (also called Sybils) on OSNs and use them for various malicious activities. Therefore, Sybil detection is a fundamental task for OSN security. Most existing Sybil detection methods are based on the graph structure of OSNs, and various methods have been proposed recently. However, although almost all methods have been compared experimentally in terms of detection performance and noise robustness, theoretical understanding of them is still lacking. In this study, we show that existing graph-based Sybil detection methods can be interpreted in a unified framework of low-pass filtering. This framework enables us to theoretically compare and analyze each method from two perspectives: filter kernel properties and the spectrum of shift matrices. Our analysis reveals that the detection performance of each method depends on the effectiveness of the low-pass filtering. Furthermore, on the basis of the analysis, we propose a novel Sybil detection method called SybilHeat. Numerical experiments on synthetic graphs and real social networks demonstrate that SybilHeat performs consistently well on graphs with various structural properties. This study lays a theoretical foundation for graph-based Sybil detection and leads to a better understanding of Sybil detection methods.

Sybils are users created for carrying out nefarious actions in online social networks (OSNs) and threaten the security of OSNs. Therefore, Sybil detection is an urgent security task, and various detection methods have been proposed. Existing Sybil detection methods are based on the relationship (i.e., graph structure) of users in OSNs. Structure-based methods can be classified into two categories: Random Walk (RW)-based and Belief Propagation (BP)-based. However, although almost all methods have been experimentally evaluated in terms of their performance and robustness to noise, the theoretical understanding of them is insufficient. In this paper, we interpret the Sybil detection problem from the viewpoint of graph signal processing and provide a framework to formulate RW- and BPbased methods as low-pass filtering. This framework enables us to theoretically compare RW- and BP-based methods and explain why BP-based methods perform well for scale-free graphs, unlike RW-based methods. Furthermore, by this framework, we relate RW- and BP-based methods and Graph Neural Networks (GNNs) and discuss the difference among these methods. Finally, we evaluate the validity of this framework through numerical experiments.

The oscillation model on networks offers a powerful way of describing the user dynamics in online social networks. In general, the damping effect of the oscillation model should depend on the eigenfrequency of the oscillation mode, as is true for many oscillation phenomena around us. This work starts with the frequency-dependent damping effect from which we propose a method for estimating the damping coefficient, that is the strength of the damping effect, from the observed oscillation dynamics.

Recent development of online social networks (OSNs) has accelerated communication between individuals and has led to efficient activities in the real world. However, activities on OSNs does not necessarily have positive impact on the real world. For example, explosive user dynamics such as flaming on OSNs seriously impacts not only on online communities but also on social activities in the real world. Therefore, to prevent the flaming, we need to understand the cause of explosive user dynamics. We have proposed to describe user dynamics in OSNs by the wave equation on networks which called the oscillation model of OSNs [1]. Although this model was motivated from a purely theoretical framework, it gives natural extension of the conventional node centrality measures [2], [3] and enables us to understand the cause of explosive user dynamics [1], [4]. However, its experimental verification is not sufficiently conducted. This paper discuss the validity of the oscillation model by analyzing real data.

Explosive dynamics in on-line social networks including flaming phenomena give serious impacts not only on stable operation of information networks but also social activities on the real world. The oscillation model in directed networks can describe generation the flaming phenomena in on-line social networks. In this paper, by introducing perturbation analysis, we describe the effect of directed link in the network to generation of the flaming phenomena, based om the oscillation model.

A flash crowd is a situation in which a large number of users access a Web service simultaneously. Conventional methods for managing flash crowds include dropping of excessive requests, preparing of redundant servers, and dynamic provisioning of server resources. It is, however, difficult to use these methods to satisfy all the related requirements such as prevention of system down, avoidance of user abandonment, and efficient use of resources. With a focus on the psychological factor of users, we propose a method for flash crowd mitigation based on the presentation of feedback information. The proposed method was confirmed by experiment to be effective for influencing user behaviors such as abandonment and retransmission of requests. Consequently, we developed a user behavior model and used it to conduct website stress tests using a simulated website. The results further revealed that feedback information could be used to reduce the server load and user abandonment ratio. This paper also suggests the type of Web services for which feedback information-based flash crowd mitigation is most suitable.

This paper proposes an oscillation model for describing the flaming phenomena in on-line social networks, and discusses countermeasures to flaming based on the proposed oscillation model. The most significant feature of the proposed model is that the cause of flaming can be explained by the structure of the network. Based on the proposed model, we can suppress the generation of flaming by controlling link weights of a part of the network. This passive solution to flaming is important for the stable operation of social media networks.

Spectral graph theory gives a useful approach to analyzing network structure based on the adjacency matrix or the Laplacian matrix that represents the network topology and link weights. However, in large scale and complex social networks, since it is difficult to know the network topology and link weights, we cannot determine the components of these matrices directly. To solve this problem, we consider a method for indirectly determining a Laplacian matrix from its eigenvalues and eigenvectors. As the first step, our prior study proposed a method for estimating eigenvalues of a Laplacian matrix by using the resonance of oscillation dynamics on networks with no a priori information about the network structure, and showed the effectiveness of this method. In this paper, we propose a method for estimating the eigenvectors of a Laplacian matrix by once again using the resonance of oscillation dynamics on networks.

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.

In recent years, with the rapid development of the Internet and cloud computing, an enormous amount of information is exchanged on various social networking services. In order to handle and maintain such a mountain of information properly by limited resources in the network, it is very important to comprehend the dynamics for propagation of information or activity on the social network. One of many indices used by social network analysis which investigates the network structure is “Node centrality”. A common characteristic of conventional node centralities is that it depends on the topological structure of network and the value of node centrality does not change unless the topology changes. In our recent research, we have proposed an oscillation model for describing dynamics on networks generated from a certain kind of asymmetric interaction between nodes by using a symmetric matrix. Moreover, we proposed a new extended index of well-known two node centralities based on the oscillation model. In this paper, we discuss generally extension of node centralities based on the oscillation model. In addition, we show that the proposed index can describe various node centralities by properly assigning the weight of each link, and the proposed model is regarded as the fundamental framework for different node centralities.

Since recent rapid development of information network technology activates the information exchange on the social network, dynamics for propagation of information or activity on the social network is an interesting research object. The network dynamics is generated by interaction between users whose strength is asymmetric in general. Network structure reflecting the asymmetric interaction between users is modeled by a directed graph, and it is described by an asymmetric matrix in matrix-based network model. In our recent research, we have proposed an oscillation model for describing dynamics on networks generated from a certain kind of asymmetric interaction between nodes by using a symmetric matrix. In this paper, based on the oscillation model, we propose a new extended index of the node centrality. The proposed index can describe various aspect of node centrality that considers not only the topological structure of the network, but also asymmetry of links, the distribution of source node of activity, and temporal evolution of activity propagation.

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.

Active measurement, which can provide end-to-end measurements of network performance, is critical since the Internet is managed by multiple organizations. Recently, on the active measurement of delay and loss, Baccelli et al reported that many probing policies can be used to provide appropriate estimation in addition to the traditional policy based on PASTA property if the volume of probe stream is negligible compared to the traffic stream. Probing schemes with fixed probe packet intervals suffer from the phase-lock phenomenon due to synchronization against the network performance; they do, however, provide superior accuracy. A remaining issue is how to decide the optimal probing policy while taking the phase-lock phenomenon into consideration. In this paper, we propose the probing policy that randomly fluctuates the probe packet interval to avoid the phase-lock phenomenon. We start by clarifying the relationships among the fluctuation magnitude, the properties of the target network, and estimation accuracy, and we discuss the optimal probing policy with regard to the properties of the target network.

We have already proposed the framework of autonomous decentralized control based on local-interaction as a novel control mechanism for communication networks. This framework is based on the relation between local interaction and the solution yielded by a partial differential equation. In this framework, the state of the complete system is controlled by appropriately designing the autonomous operation of the subsystems. That is, the local action rules (micro-level) are designed to produce an appropriate state of the whole system at the macro-level. Our previous studies proposed diffusion type flow control (DFC) as a solution for the extremely time sensitive flow control required by high-speed networks and we have evaluated the performance of DFC by simulation. In this paper, to apply DFC to real networks, we show the technique to implement the DFC functions on PCs and to investigate its performance in the presence of TCP.

We have proposed diffusion flow control (DFC) as a technique for controlling a network, using an analogy of the diffusion phenomenon. In DFC, although each node in a network handles its local traffic flow only on the basis of the information it is aware of, the diffusion effect of DFC distributes the packet density in a network, so avoiding packet loss. In our previous work, we have assumed that each node knows the lengths of the links adjacent to it and we used control timing depending on the link length. In this paper, we describe an extension of the DFC in which the control timing is independent of the link length so allowing DFC to be used in a mobile ad hoc network.

One problem with active measurement is that, while it is suitable for measuring time-average network performance, it is difficult to measure per-flow quality of service (QoS), which is defined as the average over packets in the flow. To achieve such per-flow QoS measurement, the authors proposed a new technique, called the change- of- measure-based passive/active monitoring (CoMPACT Monitor), which is based on the change-of-measure framework in probability/measure theory and transforms actively obtained information by using passively monitored data. This technique enables us to concurrently measure one-way delay information about individual users, applications, and organizations in detail in a lightweight manner. This paper presents the mathematical formulation for the CoMPACT Monitor and verifies that it works well under some weak conditions. In addition, we investigate its characteristics regarding several implementation issues through simulation and actual network experiments. The results reveal that our technique provides highly qualified estimates involving only a limited amount of extra traffic from active probes.

It has been recognized that in the current Internet it is important to control not only congestion caused by overload of the data plane but also congestion caused by overload of the control plane. In particular, it is important to avoid congestion from retrial traffic. This paper focuses on RSVP-based communication services. In general, users reattempt to set up connections not only when transmission systems are overloaded but also when processing systems in the network are overloaded. The latter is caused by user psychology: an increase in the waiting time for the processing to be completed tends to increase his or her reattempts. Thus, it is important to manage both transmission and processing resources properly. In this paper, we introduce a model that takes account of both transmission and processing systems, and we examine the behavior of retrial traffic. In addition, we compare the system stability achieved by two different resource management methods, the hard-state method and the soft-state method.

The importance of security has been recognized in ad hoc networks for many years. Consequently many secure routing methods have been proposed in this field. This paper discusses major security attacks in ad hoc networks, and proposes a number of prevention methods for resource exhaustion attacks that have severe negative effects on targeted ad hoc networks

Many studies of social networks have recently been published. Interest in topological structures, such as scale-free characteristics, has been particularly strong. In this paper, we focus on the analysis of macro traffic data in a communications network of cellular phone users as a way of investigating large-scale social networks. Behaviors of information exchange between pairs of cellular phone users are reflected in traffic data, which thus reflects interesting features of social networks. We analyze the relationship between the number of customers and the volume of traffic with a view to finding clues about the structure of social networks among the very large set of potential customers. We then demonstrate some interesting features that our analysis reveals: a scale-free topology of human relations, their cluster structures, and behaviors of user-dynamics. In addition, we consider the relationship between traffic volume and the number of customers depending on the situation.

One of the most important issues in sensor networks is to develop the technology for improved fault tolerance. From this viewpoint, the placement of sensor nodes is critical. To date, we have proposed stochastic node placements whose degrees of nodes follow a power-law ("power-law placement"). To demonstrate the effectiveness of power-law placement, we have to show that this placement has high fault tolerance and can be achieved with a reasonable degree of complexity and accuracy. In the first step of our research, we have shown that power-law placement with well-tuned parameters is more robust against failure than general stochastic node placement. In the second step to prove the effectiveness of power-law placement, we investigate ways of achieving power-law placement in this paper.

We previously proposed a change-of-measure based performance measurement method which combines active and passive measurement to estimate user-experienced performance. We also applied this method to packet-delay estimation. We apply this method to loss-rate estimation. Because packet loss rarely occurs in current networks, its measurement usually requires a huge number of probe packets, which imposes a non-negligible load on the networks. We propose a loss-rate estimation method which requires significantly fewer probe packets. In our proposed method, the correlation between delay and loss is measured in advance, and at the time of measurement, the time-averaged loss rate is estimated by using the delay of probe packets and the correlation. We have also applied our change-of-measure framework to estimating the loss rate in user packets by using this time-averaged loss rate. We prove that the mean square error in our method is lower than the simple loss measurement which is estimated by dividing the number of lost packets by the total number of sent packets. We evaluate our method through simulations and actual measurements and find that it can estimate below 10/sup -3/ packet loss rate with only 900 probe packets.

In this paper, we are studying the capacity dimensioning of virtual private networks (VPN) access-links for elastic traffic, such as the Web or ftp. Under the assumption that the core-VPN network is provisioned with sufficient large capacity, the capacity management of the VPN access link is a matter of bandwidth-sharing for elastic traffic of the two bottleneck links, the ingress and egress access links, the processor -sharing model gives a simple formulae of mean transfer time, but in our case, the value may be less than the actual transfer time. In contrast, max-min fair sharing provides an accurate sharing model which is similar to the TCP, but it is difficult to obtain a closed form of performance statistics. We propose a closed form approximation for a max-min fair sharing model, in a specific but realistic topology, by investigating the difference between the max-min and the processor sharing model. Using the approximation, we are performing the capacity dimensioning of VPN access links.

To make a scalable and lightweight QoS monitoring system, we have proposed a new QoS monitoring technique, change-of-measure based passive/active monitoring (CoMPACT monitor), which is based on change-of-measure framework and is an active measurement transformed by using passively monitored data. This technique enables us to measure detailed QoS information for individual users, applications, and organizations, in a scalable and lightweight manner. In this paper, we present the mathematical foundation of CoMPACT monitor. In addition, we show its characteristics through simulations in terms of typical implementation issues for inferring the delay distributions. The results show that CoMPACT monitor gives accurate QoS estimations with only a small amount of extra traffic for active measurement.

This paper focuses on flow control in high-speed networks. Each node in a network handles its local traffic flow only on the basis of the information it is aware of, but it is preferable that the decision-making of each node leads to high performance of the whole network. To this end, we investigate the relationship between the flow control mechanism of each node and network performance. We consider the situation in which the capacity of a link in the network is changed but individual nodes are not aware of this. Then we investigate the stability and adaptability of the network performance when the capacity of a link is changed, and discuss an appropriate flow control model on the basis of simulation results.

We propose a performance measurement method that uses both active and passive measurement data to infer the actual performance seen by users. With this method, the performance for individual users, organizations or applications can also be estimated. An actual implementation of the proposed method is examined through simulation. We also evaluated the estimation accuracy with respect to both the measurement interval and the number of measurements.

This paper investigates the stochastic property of the packet destinations and proposes an address generation algorithm which is applicable for describing various Internet access patterns. We assume that a stochastic process of Internet access satisfies the stationary condition and derive the fundamental structure of the address generation algorithm. The pseudo IP-address sequence generated from our algorithm gives dependable cache performance and reproduces the results obtained from trace-driven simulation. The proposed algorithm is applicable not only to the destination IP address but also to the destination URLs of packets, and is useful for simulation studies of Internet performance, Web caching, DNS, and so on.

This paper discuses how to design the capacity of address cache tables for large-scale computer communication networks. We show that destination addresses of packets can, be assumed to be characterized by two types of Zipf's law. Based on the complementary use of these laws, we derive the relation between the number of accesses and the number of destination addresses. Experimental results show that the relation gives a good approximation. Applying this relation, we derive the upper/lower bounds for cache hit probability. Using the probabilities, design issues including the capacity of the cache table and aging algorithm of cache entries are also discussed.

The authors address the problem of optimal routing in packet switched networks. Optimality is discussed in terms of end-to-end delay. The variance of delay as well as its mean value are taken into account. Achieving optimal routing is necessary for multimedia networks to fully support real-time services. A network is modeled as a weighted graph with its link weights representing link delays. It is assumed that the delay statistics conform to a normal distribution. In the course of analysis, it is shown that this type of routing optimization problem can be formulated as a process of searching for a specific point in a coordinate system defined by the mean and variance of the end-to-end delay. An efficient algorithm is presented for finding the optimal point in this coordinate system.>