Infected Nodes Research Articles

Non Stationary Markov Perfect Equilibria in Mean-Field Games

In this paper, we consider both finite and infinite horizon discounted dynamic mean-field games where there is a large population of homogeneous players sequentially making strategic decisions and each player is affected by other players through an aggregate population state. Each player has a private type that only she observes. Such games have been studied in the literature under simplifying assumption that population state dynamics are stationary. In this paper, we consider non-stationary population state dynamics and present a novel backward recursive algorithm to compute Markov perfect equilibrium (MPE) that depend on both, a player's private type, and current (dynamic) population state. We present sufficient conditions for existence of equilibria. Using this algorithm, we study a security problem in cyber-physical system where infected nodes put negative externality on the system, and each node makes a decision to get vaccinated. We numerically compute MPE of the game.

Optimal Containment of Epidemics over Temporal Activity-Driven Networks

In this paper, we study the dynamics of epidemic processes taking place in temporal and adaptive networks. Building on the activity-driven network model, we propose an adaptive model of epidemic processes, where the network topology dynamically changes due to both exogenous factors independent of the epidemic dynamics as well as endogenous preventive measures adopted by individuals in response to the state of the infection. A direct analysis of the model using Markov processes involves the spectral analysis of a transition probability matrix whose size grows exponentially with the number of nodes. To overcome this limitation, we derive an upper-bound on the decay rate of the number of infected nodes in terms of the eigenvalues of a $2 \times 2$ matrix. Using this upper bound, we propose an efficient algorithm to tune the parameters describing the endogenous preventive measures in order to contain epidemics over time. We confirm our theoretical results via numerical simulations.

A Method to Improve the Security of Information Diffusion in Complex Networks—Node Trust-Value Management Mechanism

The transmission of malicious information is often accompanied with normal information flows in complex networks. For the intrusion of unknown malicious information, if there is no effective defensive method to suppress its diffusion, the network may be confronted with incredible catastrophe. In this paper, to enhance the security of information diffusion in complex networks, a node trust-value management mechanism (NTVMM) is proposed. Basically, node trust value and threshold are assigned with node importance at the beginning. According to node benefit and loss in information diffusion, the two algorithms, node trust-value updated algorithm (NTVUA) and node trust-threshold updated algorithm (NTTUA), are devised to dynamically update node trust value and trust threshold, respectively. Compared with the existing node quarantine and edge blockage schemes, NTVMM only relies on the trust relationship of nodes to suppress malicious information diffusion with no need for knowing the global infection information of the network. Besides, to further make a trade-off between information security and information loss, NTVMM is associated with the classical information diffusion model to obtain the most appropriate trust threshold. Finally, we devise the network profit function to evaluate the profits brought by different methods. The simulations are respectively carried out in two synthetic complex networks and a real-world complex network. The results of simulation demonstrate that when malicious information invades the network, NTVMM can reduce the number of infected nodes in the network and enhance the security of information diffusion. Moreover, compared with node quarantine and edge blockage schemes, the proposed scheme can obtain a better network profit, and make a trade-off between information security and information loss.

Influence Minimization Algorithm Based on Coordination Game

Influence analysis is the basic technology for predicting potentially hazardous behavior and determining the traceability of the hazardous behavior in the public security domain. Previous research has focused on maximizing the diffusion of the influence; however, little research has been performed on the method of minimizing the influence of negative information dissemination in networks. This paper proposes an influence minimization algorithm based on coordinated game. When the negative information is generated in the network and some initial nodes have been infected, the goal is to minimize the number of the final infected nodes by discovering and blocking the K uninfected nodes. First, the algorithm assumes that the behavior of the node propagating information depends on the coordination game with its neighboring nodes. Second, based on the local interaction model between the nodes, this paper quantifies the level of the influence of a node that is affected by its neighbors. Finally, the heuristic algorithm is used to identify the approximate optimal solution. The results of experiments performed on four real network datasets show that the proposed algorithm can suppress negative information diffusion better than the five considered existing algorithms.

Emergent Open-Endedness from Contagion of the Fittest

In this paper, we study emergent irreducible information in populations of randomly generated computable systems that are networked and follow a Susceptible-Infected-Susceptible contagion model of imitation of the fittest neighbor. We show that there is a lower bound for the stationary prevalence (or average density of infected nodes) that triggers an unlimited increase of the expected local emergent algorithmic complexity (or information) of a node as the population size grows. We call this phenomenon expected (local) emergent open-endedness. In addition, we show that static networks with a power-law degree distribution following the Barabasi-Albert model satisfy this lower bound and, thus, display expected (local) emergent open-endedness.

A method for determining information diffusion cascades on social networks

Information diffusion on social networks has many potential real-world applications such as online marketing, e-government campaigns, and predicting large social events. Modeling information diffusion is therefore a crucial task in order both to understand its diffusion mechanism and to better control it. Our research aims at finding what factors might influence people in adopting a piece of information that is being shared on a social network. In this study, the traditional independent cascade model for information diffusion is extended with discrete time steps. The proposed model is capable of incorporating three different sources of diffusion influence: user-user influence, user-content preference, and external influence. Specifically, these sources of influence are quantified into real values of diffusion probability. To calculate user-user influence, we adopt and extend the disease transmission model according to the role of the user who diffuses the content. User-content preference, which measures the correlation between user preference and the adopted contents, is calculated based on a topic-based model. External influence is detected in a diffusion time step and is quantified and incorporated into our model for the next diffusion time step by applying and solving a logistic function. Moreover, the process of information diffusion is characterized by constructing a tree of information adoption and the diffusion scale is quantified by predicting the number of infected nodes. It is found that these sources of influence, especially external influence, play a significant role in information diffusion and eventually affect the shape and size of the diffusion cascade. The model is validated on both synthetic and real-world datasets. Experimental results confirm the advantage of our proposed method, which significantly improves over the previous models in terms of prediction accuracy

Credit Risk Diffusion in Supply Chain Finance: A Complex Networks Perspective

The diffusion of credit risk in a supply chain finance network can cause serious consequences. Using the “1 + M + N” complex network model with BA scale-free characteristics, this paper studies the credit risk diffusion in a supply chain finance network, where the credit risk diffusion process is simulated by the SIS epidemic model. We examine the impacts of various key factors, including the general financing ratio, cure time, network structure, and network scale on the credit risk diffusion process. It is found that credit risk diffusion rarely occurs in a network with a low average degree. When the average degree of the network increases, the occurrence of the credit risk diffusion becomes more frequent. Besides, the degree of the initially infected nodes with credit risk does not affect the density of the infected nodes in the steady state, while a higher degree of the cure nodes helps restrain the diffusion of credit risk in the supply chain finance network. Finally, the simulation result based on the supply chain finance network with a core node indicates that the diffusion of the credit risk diffusion in sparse supply chain finance networks with low average degrees is unstable. The results provide better understandings on the credit risk diffusion in supply chain finance networks.

Controlling epidemic outbreak based on local dynamic infectiousness on complex networks.

Resources are limited in epidemic containment; how to optimally allocate the limited resources in suppressing the epidemic spreading has been a challenging problem. To find an effective resource allocation strategy, we take the infectiousness of each infected node into consideration. By studying the interplay between the resource allocation and epidemic spreading, we find that the spreading dynamics of epidemic is affected by the preferential resource allocation. There are double phase transitions of the fraction of infected nodes, which are different from the classical epidemic model. More importantly, we find that the preferential resource allocation has double-edged sword effects on the disease spreading. When there is a small transmission rate, the infected fraction at the steady state decreases with the increment of degree of resource allocation preference, which indicates that resources of the healthy nodes should be allocated preferentially to the high infectious nodes to constrain the disease spreading. Moreover, when there is a large transmission rate, the fraction of infected nodes at the steady state increases with the increment of the degree of the preference, but the resource allocation is determined by the stage of epidemic spreading. Namely, in the early stage of the disease spreading, resources should be allocated preferentially to the high infectious nodes similar to the case of a small transmission rate. While after the early stage, resources should be allocated to the low infectious nodes. Based on the findings, we propose a simple resource allocation strategy that can adaptively change with the current fraction of infected nodes and the disease can be suppressed to the most extent under the proposed strategy.

A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things

In this paper, the problem of misinformation propagation is studied for an Internet of Battlefield Things (IoBT) system, in which an attacker seeks to inject false information in the IoBT nodes in order to compromise the IoBT operations. In the considered model, each IoBT node seeks to counter the misinformation attack by finding the optimal probability of accepting given information that minimizes its cost at each time instant. The cost is expressed in terms of the quality of information received as well as the infection cost. The problem is formulated as a mean-field game with multiclass agents, which is suitable to model a massive heterogeneous IoBT system. For this game, the mean-field equilibrium is characterized, and an algorithm based on the forward backward sweep method is proposed to find the mean-field equilibrium. Then, the finite-IoBT case is considered, and the conditions of convergence of the equilibria in the finite case to the mean-field equilibrium are presented. Numerical results show that the proposed scheme can achieve a 1.2-fold increase in the quality of information compared with a baseline scheme, in which the IoBT nodes are always transmitting. The results also show that the proposed scheme can reduce the proportion of infected nodes by 99% compared with the baseline.

Immune activation and regulatory T cells in Mycobacterium tuberculosis infected lymph nodes

BackgroundLymph node tuberculosis (LNTB) is the most frequent extrapulmonary form of tuberculosis (TB). Studies of human tuberculosis at sites of disease are limited. LNTB provides a unique opportunity to compare local in situ and peripheral blood immune response in active Mycobacterium tuberculosis (Mtb) disease. The present study analysed T regulatory cells (Treg) frequency and activation along with CD4+ T cell function in lymph nodes from LNTB patients.ResultsLymph node mononuclear cells (LNMC) were compared to autologous peripheral blood mononuclear cells (PBMC). LNMC were enriched for CD4+ T cells with a late differentiated effector memory phenotype. No differences were noted in the frequency and mutifunctional profile of memory CD4+ T cells specific for Mtb. The proportion of activated CD4+ and Tregs in LNMC was increased compared to PBMC. The correlation between Tregs and activated CD4+ T cells was stronger in LNMC than PBMC. Tregs in LNMC showed a strong positive correlation with Th1 cytokine production (IL2, IFNγ and TNFα) as well as MIP-1α after Mtb antigen stimulation. A subset of Tregs in LNMC co-expressed HLA-DR and CD38, markers of activation.ConclusionFurther research will determine the functional relationship between Treg and activated CD4+ T cells at lymph node sites of Mtb infection.

Spreading in scale-free computer networks with improved clustering

In this study, we investigated data spreading in computer networks with scale-free topology under various levels of improved clustering. Starting from a pure Barabási–Albert (BA) network topology, we applied a Poisson-based rewiring procedure with increasing rewiring probability, which promotes local connections. We then performed wired computer network simulations in NS2 simulator for these topologies. We found that for pure BA network, data transfer (throughput) is maximum, where time required for establishing routing scheme, end-to-end delays in data transmission and number of nodes acting in data transfer are at their minimum levels. Improving clustering increases these parameters those are at their minima. A noteworthy finding of this study is that, for moderate levels of clustering, total throughput remains close to its maximum yielding stable transfer rates, although number of infected nodes and end-to-end delay increase. This indicates that clustering promotes spreading phenomena in networks, although it increases average separation. As a result, clustering property emerges as a catalyzer in data spreading with minimal effects on the total amount of transmission.

Significance of follow‐up thoracic imaging in myasthenia gravis

AbstractObjectivesMost patients with myasthenia gravis (MG) undergo thoracic imaging at initial diagnosis. However, the follow‐up protocol is unclear. The objective of the present study was to clarify the follow‐up status and main findings of thoracic imaging in patients with MG.MethodsA total of 649 patients with disease duration ≥1 year were included in the study. The follow‐up rate and time from the initial scan to the latest thoracic imaging were evaluated. The rate of abnormality and details of abnormal findings in both non‐thymomatous MG and thymoma‐associated MG were investigated.ResultsThe follow‐up thoracic imaging rate of 145 patients with thymoma‐associated MG was 71.7%, whereas just 46.2% of 504 patients with non‐thymomatous MG underwent follow‐up imaging. Abnormal findings were detected in 8.6% of patients with non‐thymomatous MG and 26.0% of those with thymoma‐associated MG. Abnormal findings included cancer, lung infection and lymph node swelling in the former, and relapse or increased size of thymoma in the latter.ConclusionsFrequent follow up by thoracic imaging is necessary in patients with MG.

Correlated bursts in temporal networks slow down spreading

Spreading dynamics has been considered to take place in temporal networks, where temporal interaction patterns between nodes show non-Poissonian bursty nature. The effects of inhomogeneous interevent times (IETs) on the spreading have been extensively studied in recent years, yet little is known about the effects of correlations between IETs on the spreading. In order to investigate those effects, we study two-step deterministic susceptible-infected (SI) and probabilistic SI dynamics when the interaction patterns are modeled by inhomogeneous and correlated IETs, i.e., correlated bursts. By analyzing the transmission time statistics in a single-link setup and by simulating the spreading in Bethe lattices and random graphs, we conclude that the positive correlation between IETs slows down the spreading. We also argue that the shortest transmission time from one infected node to its susceptible neighbors can successfully explain our numerical results.

System-size expansion of the moments of a master equation.

We study an expansion method of the general multidimensional master equation, based on a system-size expansion of the time evolution equations of the moments. The method turns out to be more accurate than the traditional van Kampen expansion for the first and second moments, with an error that scales with system-size similar to an alternative expansion, also applied to the equations of the moments, called Gaussian approximation, with the advantage that it has less systematic errors. Besides, we analyze a procedure to find the solution of the expansion method and we show different cases where it greatly simplifies. This includes the analytical solution of the average value and fluctuations in the number of infected nodes of the SIS epidemic model in complex networks, under the degree-based approximation.

Double-edged sword effect of edge overlap on asymmetrically interacting spreading dynamics

There is a close interplay between the disease spreading on contact network and corresponding information propagation on communication network. Especially, it will be of much interest when there are certain overlaps between the topological edges of communication network and contact network. In this paper, we study the effect of inter-layer edge overlap on the asymmetrically interacting spreading dynamics of disease and information, mainly including the final fraction of infected nodes and the epidemic threshold Though extensive simulations, we find that there is a so called double-edged sword effect on epidemic spreading and information propagation. Namely, when the disease spreads faster than information, a smaller amount of overlapping edges is able to effectively inhibit the epidemic spreading. On the contrary, when the information propagates more quickly than disease spreads, the more overlapping edges are, the better the epidemic spreading is suppressed. In further, we show that the epidemic threshold is improved when the overlapping edges increase. These results can provide a significant guidance to make optimal strategies for suppressing epidemic spreading.

SIRA Computer Viruses Propagation Model: Mortality and Robustness

Among the several works based on the classical Kermack–Mckendrick’s SIR (Susceptible–Infected–Removed) epidemiological model, applied to the context of computer viruses propagation, the introduction of antidotal elements has provided the dynamics of the networks when anti-virus programs are used. The SIRA (Susceptible–Infected–Removed–Antidotal) model has shown good qualitative fitness regarding to real operation of the networks, when the mortality rate is considered zero and all the infected nodes being recovered. Here, the SIRA model is studied, considering the mortality rate as a parameter and the conditions for the existence of a disease-free equilibrium state are derived, helping the design of robust networks.

OPINION-AWARE INFLUENCE MAXIMIZATION: HOW TO MAXIMIZE A FAVORITE OPINION IN A SOCIAL NETWORK?

Influence maximization is a well-known problem in the social network analysis literature which is to find a small subset of seed nodes to maximize the diffusion or spread of information. The main application of this problem in the real-world is in viral marketing. However, the classic influence maximization is disabled to model the real-world viral marketing problem, since the effect of the marketing message content and nodes’ opinions have not been considered. In this paper, a modified version of influence maximization which is named as “opinion-aware influence maximization” (OAIM) problem is proposed to make the model more realistic. In this problem, the main objective is to maximize the spread of a desired opinion, by optimizing the message content, rather than the number of infected nodes, which leads to selection of the best set of seed nodes. A nonlinear bi-objective mathematical programming model is developed to model the considered problem. Some transformation techniques are applied to convert the proposed model to a linear single-objective mathematical programming model. The exact solution of the model in small datasets can be obtained by CPLEX algorithm. For the medium and large-scale datasets, a new genetic algorithm is proposed to cope with the size of the problem. Experimental results on some of the well-known datasets show the efficiency and applicability of the proposed OAIM model. In addition, the proposed genetic algorithm overcomes state-of-the-art algorithms.

Timing Matters: Influence Maximization in Social Networks Through Scheduled Seeding

One highly studied topic in the field of social networks is the search for influential nodes that, when seeded (i.e., activated intentionally), may further activate a large portion of the network through a viral contagion process. Indeed, various mathematical models were proposed in the literature to characterize the dynamics of such diffusion processes, and different solutions were suggested for maximizing influence under such models. However, most of these solutions focused on selecting a set of nodes to be seeded at the initial phase of the diffusion process. This paper suggests a scheduled seeding approach that aims at finding not only the best set of nodes to be seeded but also the right timing to perform these seedings. More specifically, we identify three different properties of existing contagion models that can be utilized by a scheduled approach to improve the total number of activated nodes: 1) stochastic dynamics; 2) diminishing social effect; and 3) state-dependent seeding. By analyzing each of these properties separately, we demonstrate the advantages of the scheduled seeding approach over the traditional initial seeding approach, both by theoretical and empirical evaluation. Our analysis presents an improvement of 10%–70% in the final number of infected nodes when using the scheduled seeding approach. Our findings have the potential to open up a new area of research, focusing on finding the right timing for seeding actions, thereby helping both in improving our understanding of information diffusion dynamics and in devising better strategies for influence maximization.

Hybrid resource allocation and its impact on the dynamics of disease spreading

Efficient utilization of limited resources is crucial in controlling disease outbreaks. Usually, in the process of disease control, medical resources are either controlled globally by the government or allocated locally among individuals. Here, to explore systematically which of the two strategies is more efficient in suppressing the outbreaks of disease, a hybrid strategy is proposed, in which, a tunable parameter is introduced to tune the preference of global and local resource allocation. By studying the interplay between the resource allocation and disease spreading based on a generalized susceptible–infected–susceptible (SIS) model, we show that the global resource allocation has more advantages in controlling disease than local allocation. Besides, the hybrid resource allocation has significant influence on the dynamics of disease spreading. There is first order phase transition of the final infected fraction of nodes on homogeneous networks, however, the transition type changes from hybrid transitions to first order transition at a critical value of the tunable parameter on heterogeneous networks. When considering the initial conditions, hysteresis loops appear on both homogeneous and heterogeneous networks. Using mean-field approximation and a generalized dynamical message passing (GDMP) approach, we calculate exactly the fraction of infected nodes at the steady state, as well as the epidemic thresholds and the phase diagram of the disease on the homogeneous networks and heterogeneous networks respectively.

A transmission-limit inspired immunization strategy for weighted network epidemiology

In this paper, we study a new type of immunization strategy which is applied to weighted networks. It helps us in maintaining the necessary network efficiency by limiting the transmission (lowering the weight of edges) while suppressing the spread of the epidemic. It is similar to the inflammation around the infected parts of our body, which can not only prevent the disease spreading but also protect the function of the body. First, we set the rate of transmission to be proportional to the edge weight based on the S-I epidemic spreading model. Then, we show the detailed dynamic evolution of the infected nodes which facilitates an effective epidemic control. Theoretical analysis and simulation results indicate that the new immunization strategy can be used to prevent the epidemic spreading effectively, while maintaining the high efficiency of the network.