Information Flow In Networks Research Articles

The epidemic spreading model and the direction of information flow in brain networks

The interplay between structural connections and emerging information flow in the human brain remains an open research problem. A recent study observed global patterns of directional information flow in empirical data using the measure of transfer entropy. For higher frequency bands, the overall direction of information flow was from posterior to anterior regions whereas an anterior-to-posterior pattern was observed in lower frequency bands. In this study, we applied a simple Susceptible-Infected-Susceptible (SIS) epidemic spreading model on the human connectome with the aim to reveal the topological properties of the structural network that give rise to these global patterns. We found that direct structural connections induced higher transfer entropy between two brain regions and that transfer entropy decreased with increasing distance between nodes (in terms of hops in the structural network). Applying the SIS model, we were able to confirm the empirically observed opposite information flow patterns and posterior hubs in the structural network seem to play a dominant role in the network dynamics. For small time scales, when these hubs acted as strong receivers of information, the global pattern of information flow was in the posterior-to-anterior direction and in the opposite direction when they were strong senders. Our analysis suggests that these global patterns of directional information flow are the result of an unequal spatial distribution of the structural degree between posterior and anterior regions and their directions seem to be linked to different time scales of the spreading process.

Cell signaling as a cognitive process.

Cellular identity as defined through morphology and function emerges from intracellular signaling networks that communicate between cells. Based on recursive interactions within and among these intracellular networks, dynamical solutions in terms of biochemical behavior are generated that can differ from those in isolated cells. In this way, cellular heterogeneity in tissues can be established, implying that cell identity is not intrinsically predetermined by the genetic code but is rather dynamically maintained in a cognitive manner. We address how to experimentally measure the flow of information in intracellular biochemical networks and demonstrate that even simple causality motifs can give rise to rich, context-dependent dynamic behavior. The concept how intercellular communication can result in novel dynamical solutions is applied to provide a contextual perspective on cell differentiation and tumorigenesis.

Shaping the Evolution of the Information Flow: The Centralizing Mechanisms in the Evolution of the North American Tobacco Quitline Consortium

ABSTRACTThe research reported here is an analysis of the evolution of the relationships that comprise a single public health network, focusing especially on the position of the network administrative organization (Provan and Kenis 2008) in the flow of knowledge among a large number of organizations providing similar services. Our study examines the North American Quitline Consortium (NAQC), a multi-sector network that spans the US and Canada and whose members provide telephone-based tobacco cessation services to anyone interested in quitting smoking. Data were collected using web-based surveys at three different points of time. Implications are discussed for network organizing, for both theory and practice, focusing especially on the importance of the network administrative organization in shaping the evolution of the whole network information flow.

Dynamical Modeling and Distributed Control of Connected and Automated Vehicles: Challenges and Opportunities

The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control.

Research on Cyber-Physical Modeling for Smart Distribution Grid

Smart distribution network will achieve the optimal operation of the distribution network, provide high-quality and reliable power, guarantee the development of modern social economy. The deep integration of cyber system and power physical system is the key to smart distribution network. The emergence of cyber-physical system (CPS) provides a new way to solve this problem, the cyber-physical model for smart distribution grid becomes an urgent problem to be solved. In this paper, the content and method of cyber-physical model for smart distribution grid are analyzed by combining with the coupling of information flow and power flow of smart distribution network from the perspective of cyber-physical model. At last, taking 110 kV typical substation as an example, the coupling mechanism and function of power flow and information flow is studied.

СИНЕРГЕТИЧНИЙ ЕФЕКТ ВІД ЗАСТОСУВАННЯ КОНЦЕПЦІЙ «ОЩАДЛИВОГО ВИРОБНИЦТВА» ТА «ІНДУСТРІЇ 4.0» ДЛЯ ПІДПРИЄМСТВ ТРАНСПОРТНОЇ ГАЛУЗІ

Abstract the philosophy of lean production, which consists of optimizing production processes, eliminating losses, improving the quality of goods and services, customer satisfaction, and continuous improvement, is extremely relevant today. Also, this concept is very consonant with the latest industrial revolution - Industry 4.0 (or the Fourth Industrial Revolution), which aims to make the transition from conventional automation of production, the use of information technology in production (which were the essence of the third industrial revolution) to the network of resources, information flows, objects and people. The purpose of this study was to examine the relationship between lean production paradigms and Industry 4.0. Both concepts with their tools were analyzed, their influence on each other was discovered and the possibility of integrating lean production methods with technologies of Industry 4.0 was considered on the basis of enterprises of the transport industry. This article proposes to consider the concepts of "Lean Production" and "Industry 4.0" as separate areas of enterprise development, describing its advantages and disadvantages, and the search for empirical evidence of the relationship between them. The tools and methods of the concepts of Lean Production and Industry 4.0 are investigated and compared to find the main similar and different technological characteristics. The compatibility of concepts due to the similarity of goals and principles of paradigms is considered, in particular, efficient use of resources and minimization of losses, continuous improvement, and use of technologies to automate processes. The possibility of combining the advantages of each concept to create a common flexible system to increase the efficiency of transport enterprises and the transport industry as a whole was assessed.

Sensor Network Information Flow Control Method with Static Coordinator within Internet of Things in Smart House Environment

Nowadays the technical solutions related to energy resource management are being rapidly developed and integrated into the daily lives of people. The energy resource management systems use sensor networks for receiving and processing information during the realia time. Despite the large number of research related to increasing the life expectancy of a network, node positioning, network clustering as well as the optimization of the data-processing model, issues related to the data flow management and classification of the information become more and more topical; they allow to reduce the amount of transmitted information within the network. The network load of the unstructured information flow is the most important aspect that impacts the service quality of the network as well as the life expectancy of a node; as a result, the methods of network performance optimization and increase in life expectancy in the networks with a high information transmission rate become ineffective. Nowadays the amount of transmitted information in the Internet of Things networks is increased and becomes chaotic which results in the reduction of the overall network efficiency and life expectancy. The article discusses the management method of the network data flow that can respond to a data flow programming task whilst taking into account the balancing of the node energy in the network.

NetFlowMatrix: a visual approach for analysing large NetFlow data

NetFlowMatrix is a visual analytics system design that adopts small multiple charts to help analysts monitor NetFlow data of a computer network. This design provides an overview and drill-down interactions that allow analysts to see and analyse traffic data from a computer network of thousands of computers and millions of flow records. Various network activities generate NetFlow records with different characteristics. We grouped network flow information into a matrix of cells to display aggregate flows based on payload size and flow duration. The aggregate overview method is scalable that allows the design to support much larger computer networks. To visually distinguish extreme low and high quantity of flows, we use colour shades to distinguish different scales of cells. Utilising this innovative overview design, professionals can easily identify patterns and instances, obvious or subtle, from a large number of network flows.

ATMSim: a Hadoop and self-similarity-based simulator for collecting, detecting, measuring and analysing anomalous traffic

Recent developments in information and communication networks as well as the popularity of smartphones have been contributing to a geometrical increase in internet traffic. In relation to this, this study aims to collect, detect, measure and analyse the DDoS attacks typical of increasing security incidents on internet and network attacks. To this end, a large volume of normal traffic, coming in through an internal LAN of a university, and anomalous traffic including DDoS attacks using an ATMSim analysis package operating on the basis of network flow information, was generated. The self-similarity estimation techniques were used to analyse the behaviour of the collected and generated normal and anomalous traffic. This information was then used to prove graphically and quantitatively that the analysis reveals a great difference between the normal traffic and the anomalous traffic in terms of self-similarity.

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks.

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called “Collective Influence (CI)” has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes’ significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct “virtual” information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes’ importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.

A dendritic disinhibitory circuit mechanism for pathway-specific gating.

While reading a book in a noisy café, how does your brain ‘gate in' visual information while filtering out auditory stimuli? Here we propose a mechanism for such flexible routing of information flow in a complex brain network (pathway-specific gating), tested using a network model of pyramidal neurons and three classes of interneurons with connection probabilities constrained by data. We find that if inputs from different pathways cluster on a pyramidal neuron dendrite, a pathway can be gated-on by a disinhibitory circuit motif. The branch-specific disinhibition can be achieved despite dense interneuronal connectivity, even with random connections. Moreover, clustering of input pathways on dendrites can naturally emerge through synaptic plasticity regulated by dendritic inhibition. This gating mechanism in a neural circuit is further demonstrated by performing a context-dependent decision-making task. The model suggests that cognitive flexibility engages top-down signalling of behavioural rule or context that targets specific classes of inhibitory neurons.

Reducing wildland fire hazard exploiting complex network theory: A case study analysis

We discuss a new systematic methodology to mitigate wildland fire hazard by appropriately distributing fuel breaks in space. In particular, motivated by the concept of information flow in complex networks we create a hierarchical allocation of the landscape patches that facilitate the fire propagation based on the Bonacich centrality. Reducing the fuel load in these critical patches results to lower levels of fire hazard. For illustration purposes we apply the proposed strategy to a real case of wildland fire. In particular we focus on the wildland fire that occurred in Spetses Island, Greece in 1990 and burned the one third of the forest. The efficiency of the proposed strategy is compared against the benchmark of random distribution of fuel breaks for a wide range of fuel breaks densities.

Exploring Hierarchies in Online Social Networks

Social hierarchy (i.e., pyramid structure of societies) is a fundamental concept in sociology and social network analysis. The importance of social hierarchy in a social network is that the topological structure of the social hierarchy is essential in both shaping the nature of social interactions between individuals and unfolding the structure of the social networks. The social hierarchy found in a social network can be utilized to improve the accuracy of link prediction, provide better query results, rank web pages, and study information flow and spread in complex networks. In this paper, we model a social network as a directed graph $G$ , and consider the social hierarchy as DAG (directed acyclic graph) of $G$ , denoted as $G_D$ . By DAG, all the vertices in $G$ can be partitioned into different levels, the vertices at the same level represent a disjoint group in the social hierarchy, and all the edges in DAG follow one direction. The main issue we study in this paper is how to find DAG $G_D$ in $G$ . The approach we take is to find $G_D$ by removing all possible cycles from $G$ such that $G = {\cal U}(G) \cup G_D$ , where ${\cal U}(G)$ is a maximum Eulerian subgraph which contains all possible cycles. We give the reasons for doing so, investigate the properties of $G_D$ found, and discuss the applications. In addition, we develop a novel two-phase algorithm, called Greedy-&-Refine, which greedily computes an Eulerian subgraph and then refines this greedy solution to find the maximum Eulerian subgraph. We give a bound between the greedy solution and the optimal. The quality of our greedy approach is high. We conduct comprehensive experimental studies over 14 real-world datasets. The results show that our algorithms are at least two orders of magnitude faster than the baseline algorithm.

Mimicking Directed Binary Networks for Exploring Systemic Sensitivity: Is NCAA FBS a Fragile Competition System?

Can a popular real-world competition system indeed be fragile? To address this question, we represent such a system by a directed binary network. Upon observed network data, typically in a form of win-and-loss matrix, our computational developments begin with collectively extracting network's information flows. And then we compute and discover network's macrostate. This computable macrostate is further shown to contain deterministic structures embedded with randomness mechanisms. Such coupled deterministic and stochastic components becomes the basis for generating the microstate ensemble. Specifically a network mimicking algorithm is proposed to generate a microstate ensemble by subject to the statistical mechanics principle: All generated microscopic states have to conform to its macrostate of the target system. We demonstrate that such a microstate ensemble is an effective platform for exploring systemic sensitivity. Throughout our computational developments, we employ the NCAA Football Bowl Subdivision (FBS) as an illustrating example system. Upon this system, its macrostate is discovered by having a nonlinear global ranking hierarchy as its deterministic component, while its constrained randomness component is embraced within the nearly completely recovered conference schedule . Based on the computed microstate ensemble, we are able to conclude that the NCAA FBS is overall a fragile competition system because it retains highly heterogeneous degrees of sensitivity with its ranking hierarchy.

Integrating Transcriptomic and Proteomic Data Using Predictive Regulatory Network Models of Host Response to Pathogens.

Mammalian host response to pathogenic infections is controlled by a complex regulatory network connecting regulatory proteins such as transcription factors and signaling proteins to target genes. An important challenge in infectious disease research is to understand molecular similarities and differences in mammalian host response to diverse sets of pathogens. Recently, systems biology studies have produced rich collections of omic profiles measuring host response to infectious agents such as influenza viruses at multiple levels. To gain a comprehensive understanding of the regulatory network driving host response to multiple infectious agents, we integrated host transcriptomes and proteomes using a network-based approach. Our approach combines expression-based regulatory network inference, structured-sparsity based regression, and network information flow to infer putative physical regulatory programs for expression modules. We applied our approach to identify regulatory networks, modules and subnetworks that drive host response to multiple influenza infections. The inferred regulatory network and modules are significantly enriched for known pathways of immune response and implicate apoptosis, splicing, and interferon signaling processes in the differential response of viral infections of different pathogenicities. We used the learned network to prioritize regulators and study virus and time-point specific networks. RNAi-based knockdown of predicted regulators had significant impact on viral replication and include several previously unknown regulators. Taken together, our integrated analysis identified novel module level patterns that capture strain and pathogenicity-specific patterns of expression and helped identify important regulators of host response to influenza infection.

Inference of Causal Information Flow in Collective Animal Behavior

Understanding and even defining what constitutes animal interactions remains a challenging problem. Correlational tools may be inappropriate for detecting communication between a set of many agents exhibiting nonlinear behavior. A different approach is to define coordinated motions in terms of an information theoretic channel of direct causal information flow. In this work, we consider time series data obtained by an experimental protocol of optical tracking of the insect species Chironomus riparius. The data constitute reconstructed 3-D spatial trajectories of the insects' flight trajectories and kinematics. We present an application of the optimal causation entropy (oCSE) principle to identify direct causal relationships or information channels among the insects. The collection of channels inferred by oCSE describes a network of information flow within the swarm. We find that information channels with a long spatial range are more common than expected under the assumption that causal information flows should be spatially localized. The tools developed herein are general and applicable to the inference and study of intercommunication networks in a wide variety of natural settings.

Классификация информационных потоков в сети связи для обоснования целесообразности применения к ним способов обеспечения качества обслуживания

Классификация информационных потоков в сети связи для обоснования целесообразности применения к ним способов обеспечения качества обслуживания

Oxytocin depolarizes fast-spiking hilar interneurons and induces GABA release onto mossy cells of the rat dentate gyrus.

Delivery of exogenous oxytocin (OXT) to central oxytocin receptors (OXT-Rs) is currently being investigated as a potential treatment for conditions such as post-traumatic stress disorder (PTSD), depression, social anxiety, and autism spectrum disorder (ASD). Despite significant research implicating central OXT signaling in modulation of mood, affect, social behavior, and stress response, relatively little is known about the cellular and synaptic mechanisms underlying these complex actions, particularly in brain regions which express the OXT-R but lie outside of the hypothalamus (where OXT-synthesizing neurons reside). We report that bath application of low concentrations of the selective OXT-R agonist Thr4,Gly7-OXT (TGOT) reliably and robustly drives GABA release in the dentate gyrus in an action potential dependent manner. Additional experiments led to identification of a small subset of small hilar interneurons that are directly depolarized by acute application of TGOT. From a physiological perspective, TGOT-responsive hilar interneurons have high input resistance, rapid repolarization velocity during an action potential, and a robust afterhyperpolarization. Further, they fire irregularly (or stutter) in response to moderate depolarization, and fire quickly with minimal spike frequency accommodation in response to large current injections. From an anatomical perspective, TGOT responsive hilar interneurons have dense axonal arborizations in the hilus that were found in close proximity with mossy cell somata and/or proximal dendrites, and also invade the granule cell layer. Further, they have primary dendrites that always extend into the granule cell layer, and sometimes have clear arborizations in the molecular layer. Overall, these data reveal a novel site of action for OXT in an important limbic circuit, and represent a significant step towards better understanding how endogenous OXT may modulate flow of information in hippocampal networks. © 2016 Wiley Periodicals, Inc.

Automated design, verification and testing of secure systems with embedded devices based on elicitation of expert knowledge

The rising significance and widening of embedded systems stipulate the importance of the security means against a great deal of computer security threats. Such systems involving a diversity of an-hoc embedded and mobile electronic devices functioning with the use of a broadband Internet access and even cloud technologies, are referenced conventionally as Internet of Things systems (IoT). Due to specificity of IoT systems the application of the combined security mechanisms requires their efficient energy and computing resource consumption, identification of potential conflicts and incompatibilities, control of information flows, monitoring anomalies of data in the system and other issues. At that an increased design complexity of IoT systems is determined by a low structuring and formalization of security knowledge in the field. We proposed an approach to identification of embedded security expert knowledge for its subsequent use in automated design, verification and testing tools for secure IoT systems. The paper encompasses the core elements of the proposed technique, namely security component configuring, revelation of implicit conflicts, verification of network information flows and abnormal data from sensors. The domain specific analysis of the field of embedded security is described. We also present the revealed expert knowledge used for configuration, verification and testing of embedded devices. Issues of software implementation and discussion are covered.

Functional connectivity and information flow of the respiratory neural network in chronic obstructive pulmonary disease.

Breathing involves a complex interplay between the brainstem automatic network and cortical voluntary command. How these brain regions communicate at rest or during inspiratory loading is unknown. This issue is crucial for several reasons: (i) increased respiratory loading is a major feature of several respiratory diseases, (ii) failure of the voluntary motor and cortical sensory processing drives is among the mechanisms that precede acute respiratory failure, (iii) several cerebral structures involved in responding to inspiratory loading participate in the perception of dyspnea, a distressing symptom in many disease. We studied functional connectivity and Granger causality of the respiratory network in controls and patients with chronic obstructive pulmonary disease (COPD), at rest and during inspiratory loading. Compared with those of controls, the motor cortex area of patients exhibited decreased connectivity with their contralateral counterparts and no connectivity with the brainstem. In the patients, the information flow was reversed at rest with the source of the network shifted from the medulla towards the motor cortex. During inspiratory loading, the system was overwhelmed and the motor cortex became the sink of the network. This major finding may help to understand why some patients with COPD are prone to acute respiratory failure. Network connectivity and causality were related to lung function and illness severity. We validated our connectivity and causality results with a mathematical model of neural network. Our findings suggest a new therapeutic strategy involving the modulation of brain activity to increase motor cortex functional connectivity and improve respiratory muscles performance in patients. Hum Brain Mapp 37:2736–2754, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.