Scalable harmonization of complex networks with local controllers

Computational and communication complexities call for distributed, robust, and adaptive control. This paper proposes a promising way of bottom-up design of distributed control in which simple controllers are responsible for individual nodes. The overall behavior of the network can be achieved by interconnecting such controlled loops in cascade control for example and by enabling the individual nodes to share information about data with their neighbors without aiming at unattainable global solution. The problem is addressed by employing a fully probabilistic design, which can cope with inherent uncertainties, that can be implemented adaptively and which provide a systematic rich way to information sharing. This paper elaborates the overall solution, applies it to linear-Gaussian case, and provides simulation results.

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

The interplay between microscopic and mesoscopic structures in complex networks

Understanding a complex network's structure holds the key to understanding its function. The physics community has contributed a multitude of methods and analyses to this cross-disciplinary endeavor. Structural features exist on both the microscopic level, resulting from differences between single node properties, and the mesoscopic level resulting from properties shared by groups of nodes. Disentangling the determinants of network structure on these different scales has remained a major, and so far unsolved, challenge. Here we show how multiscale generative probabilistic exponential random graph models combined with efficient, distributive message-passing inference techniques can be used to achieve this separation of scales, leading to improved detection accuracy of latent classes as demonstrated on benchmark problems. It sheds new light on the statistical significance of motif-distributions in neural networks and improves the link-prediction accuracy as exemplified for gene-disease associations in the highly consequential Online Mendelian Inheritance in Man database. © 2011 Reichardt et al.

Building cliques and alliances as practices to 'make things happen' in complex networks

The Roma population has become a policy issue highly debated in the European Union (EU). The EU acknowledges that this ethnic minority faces extreme poverty and complex social and economic problems. 52% of the Roma population live in extreme poverty, 75% in poverty (Soros Foundation, 2007, p. 8), with a life expectancy at birth of about ten years less than the majority population. As a result, Romania has received a great deal of policy attention and EU funding, being eligible for 19.7 billion Euros from the EU for 2007-2013. Yet progress is slow; it is debated whether Romania's government and companies were capable to use these funds (EurActiv.ro, 2012). Analysing three case studies, this research looks at policy implementation in relation to the role of Roma networks in different geographical regions of Romania. It gives insights about how to get things done in complex settings and it explains responses to the Roma problem as a „wicked‟ policy issue. This longitudinal research was conducted between 2008 and 2011, comprising 86 semi-structured interviews, 15 observations, and documentary sources and using a purposive sample focused on institutions responsible for implementing social policies for Roma: Public Health Departments, School Inspectorates, City Halls, Prefectures, and NGOs. Respondents included: governmental workers, academics, Roma school mediators, Roma health mediators, Roma experts, Roma Councillors, NGOs workers, and Roma service users. By triangulating the data collected with various methods and applied to various categories of respondents, a comprehensive and precise representation of Roma network practices was created. The provisions of the 2001 „Governmental Strategy to Improve the Situation of the Roma Population‟ facilitated forming a Roma network by introducing special jobs in local and central administration. In different counties, resources, people, their skills, and practices varied. As opposed to the communist period, a new Roma elite emerged: social entrepreneurs set the pace of change by creating either closed cliques or open alliances and by using more or less transparent practices. This research deploys the concept of social/institutional entrepreneurs to analyse how key actors influence clique and alliance formation and functioning. Significantly, by contrasting three case studies, it shows that both closed cliques and open alliances help to achieve public policy network objectives, but that closed cliques can also lead to failure to improve the health and education of Roma people in a certain region.

The value of indirect ties in citation networks:SNA analysis with OWA operator weights

This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations. It applies social network analysis (SNA) and the ordered weighted averaging operator (OWA) to study a patent citations network. So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties. In this research OWA is used to analyse complex networks, assess the role of indirect ties, and provide guidance to reduce complexity for decision makers and analysts. An empirical example of a set of European patents published in 2000 in the renewable energy industry is provided to show the usefulness of the proposed approach for the preference ranking of patent citations.

Estimating conditional volatility with neural networks

It is well known that one of the obstacles to effective forecasting of exchange rates is heteroscedasticity (non-stationary conditional variance). The autoregressive conditional heteroscedastic (ARCH) model and its variants have been used to estimate a time dependent variance for many financial time series. However, such models are essentially linear in form and we can ask whether a non-linear model for variance can improve results just as non-linear models (such as neural networks) for the mean have done. In this paper we consider two neural network models for variance estimation. Mixture Density Networks (Bishop 1994, Nix and Weigend 1994) combine a Multi-Layer Perceptron (MLP) and a mixture model to estimate the conditional data density. They are trained using a maximum likelihood approach. However, it is known that maximum likelihood estimates are biased and lead to a systematic under-estimate of variance. More recently, a Bayesian approach to parameter estimation has been developed (Bishop and Qazaz 1996) that shows promise in removing the maximum likelihood bias. However, up to now, this model has not been used for time series prediction. Here we compare these algorithms with two other models to provide benchmark results: a linear model (from the ARIMA family), and a conventional neural network trained with a sum-of-squares error function (which estimates the conditional mean of the time series with a constant variance noise model). This comparison is carried out on daily exchange rate data for five currencies.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Clustering algorithm based on minimal path loss ratio for vehicular communication

Emerging vehicular comfort applications pose a host of completely new set of requirements such as maintaining end-to-end connectivity, packet routing, and reliable communication for internet access while on the move. One of the biggest challenges is to provide good quality of service (QoS) such as low packet delay while coping with the fast topological changes. In this paper, we propose a clustering algorithm based on minimal path loss ratio (MPLR) which should help in spectrum efficiency and reduce data congestion in the network. The vehicular nodes which experience minimal path loss are selected as the cluster heads. The performance of the MPLR clustering algorithm is calculated by rate of change of cluster heads, average number of clusters and average cluster size. Vehicular traffic models derived from the Traffic Wales data are fed as input to the motorway simulator. A mathematical analysis for the rate of change of cluster head is derived which validates the MPLR algorithm and is compared with the simulated results. The mathematical and simulated results are in good agreement indicating the stability of the algorithm and the accuracy of the simulator. The MPLR system is also compared with V2R system with MPLR system performing better. © 2013 IEEE.

Clustering algorithm based on minimal path loss ratio for vehicular communication

Emerging vehicular comfort applications pose a host of completely new set of requirements such as maintaining end-to-end connectivity, packet routing, and reliable communication for internet access while on the move. One of the biggest challenges is to provide good quality of service (QoS) such as low packet delay while coping with the fast topological changes. In this paper, we propose a clustering algorithm based on minimal path loss ratio (MPLR) which should help in spectrum efficiency and reduce data congestion in the network. The vehicular nodes which experience minimal path loss are selected as the cluster heads. The performance of the MPLR clustering algorithm is calculated by rate of change of cluster heads, average number of clusters and average cluster size. Vehicular traffic models derived from the Traffic Wales data are fed as input to the motorway simulator. A mathematical analysis for the rate of change of cluster head is derived which validates the MPLR algorithm and is compared with the simulated results. The mathematical and simulated results are in good agreement indicating the stability of the algorithm and the accuracy of the simulator. The MPLR system is also compared with V2R system with MPLR system performing better. © 2013 IEEE.

Indirect ties in knowledge networks:a social network analysis with ordered weighted averaging operators

This PhD thesis analyses networks of knowledge flows, focusing on the role of indirect ties in the knowledge transfer, knowledge accumulation and knowledge creation process. It extends and improves existing methods for mapping networks of knowledge flows in two different applications and contributes to two stream of research. To support the underlying idea of this thesis, which is finding an alternative method to rank indirect network ties to shed a new light on the dynamics of knowledge transfer, we apply Ordered Weighted Averaging (OWA) to two different network contexts. Knowledge flows in patent citation networks and a company supply chain network are analysed using Social Network Analysis (SNA) and the OWA operator. The OWA is used here for the first time (i) to rank indirect citations in patent networks, providing new insight into their role in transferring knowledge among network nodes; and to analyse a long chain of patent generations along 13 years; (ii) to rank indirect relations in a company supply chain network, to shed light on the role of indirectly connected individuals involved in the knowledge transfer and creation processes and to contribute to the literature on knowledge management in a supply chain. In doing so, indirect ties are measured and their role as means of knowledge transfer is shown. Thus, this thesis represents a first attempt to bridge the OWA and SNA fields and to show that the two methods can be used together to enrich the understanding of the role of indirectly connected nodes in a network. More specifically, the OWA scores enrich our understanding of knowledge evolution over time within complex networks. Future research can show the usefulness of OWA operator in different complex networks, such as the on-line social networks that consists of thousand of nodes.

On the k-anonymization of time-varying and multi-layer social graphs

The popularity of online social media platforms provides an unprecedented opportunity to study real-world complex networks of interactions. However, releasing this data to researchers and the public comes at the cost of potentially exposing private and sensitive user information. It has been shown that a naive anonymization of a network by removing the identity of the nodes is not sufficient to preserve users’ privacy. In order to deal with malicious attacks, k -anonymity solutions have been proposed to partially obfuscate topological information that can be used to infer nodes’ identity. In this paper, we study the problem of ensuring k anonymity in time-varying graphs, i.e., graphs with a structure that changes over time, and multi-layer graphs, i.e., graphs with multiple types of links. More specifically, we examine the case in which the attacker has access to the degree of the nodes. The goal is to generate a new graph where, given the degree of a node in each (temporal) layer of the graph, such a node remains indistinguishable from other k-1 nodes in the graph. In order to achieve this, we find the optimal partitioning of the graph nodes such that the cost of anonymizing the degree information within each group is minimum. We show that this reduces to a special case of a Generalized Assignment Problem, and we propose a simple yet effective algorithm to solve it. Finally, we introduce an iterated linear programming approach to enforce the realizability of the anonymized degree sequences. The efficacy of the method is assessed through an extensive set of experiments on synthetic and real-world graphs.

A comparative study of 'joined-up' working in three regeneration programme case studies

This research concerns the development of coordination and co-governance within three different regeneration programmes within one Midlands city over the period from 1999 to 2002. The New Labour government, in office since 1997, had an agenda for ‘joining-up’ government, part of which has had considerable impact in the area of regeneration policy. Joining-up government encompasses a set of related activities which can include the coordination of policy-making and service delivery. In regeneration, it also includes a commitment to operate through co-governance. Central government and local and regional organisations have sought to put this idea into practice by using what may be referred to as network management processes. Many characteristics of new policies are designed to address the management of networks. Network management is not new in this area, it has developed at least since the early 1990s with the City Challenge and Single Regeneration Budget (SRB) programmes as a way of encouraging more inclusive and effective regeneration interventions. Network management theory suggests that better management can improve decision-making outcomes in complex networks. The theories and concepts are utilised in three case studies as a way of understanding how and why regeneration attempts demonstrate real advances in inter-organisational working at certain times whilst faltering at others. Current cases are compared to the historical case of the original SRB programme as a method of assessing change. The findings suggest that: The use of network management can be identified at all levels of governance. As previous literature has highlighted, central government is the most important actor regarding network structuring. However, it can be argued that network structuring and game management are both practised by central and local actors; Furthermore, all three of the theoretical perspectives within network management (Instrumental, Institutional and Interactive), have been identified within UK regeneration networks. All may have a role to play with no single perspective likely to succeed on its own. Therefore, all could make an important contribution to the understanding of how groups can be brought together to work jointly; The findings support Klijn’s (1997) assertion that the institutional perspective is dominant for understanding network management processes; Instrumentalism continues on all sides, as the acquisition of resources remains the major driver for partnership activity; The level of interaction appears to be low despite the intentions for interactive decision-making; Overall, network management remains partial. Little attention is paid to the issues of accountability or to the institutional structures which can prevent networks from implementing the policies designed by central government, and/or the regional tier.

Does money matter in inflation forecasting

This paper provides the most fully comprehensive evidence to date on whether or not monetary aggregates are valuable for forecasting US inflation in the early to mid 2000s. We explore a wide range of different definitions of money, including different methods of aggregation and different collections of included monetary assets. In our forecasting experiment we use two non-linear techniques, namely, recurrent neural networks and kernel recursive least squares regression - techniques that are new to macroeconomics. Recurrent neural networks operate with potentially unbounded input memory, while the kernel regression technique is a finite memory predictor. The two methodologies compete to find the best fitting US inflation forecasting models and are then compared to forecasts from a naive random walk model. The best models were non-linear autoregressive models based on kernel methods. Our findings do not provide much support for the usefulness of monetary aggregates in forecasting inflation.

Multiple roles of charged amino acids in cytoplasmic loop 7 for expression and function of the multidrug and organic anion transporter MRP1 (ABCC1)

Multidrug resistance protein MRP1 mediates the ATP-dependent efflux of many chemotherapeutic agents and organic anions. MRP1 has two nucleotide binding sites (NBSs) and three membrane spanning domains (MSDs) containing 17 transmembrane helices linked by extracellular and cytoplasmic loops (CL). Homology models suggest that CL7 (amino acids 1141-1195) is in a position where it could participate in signaling between the MSDs and NBSs during the transport process. We have individually replaced eight charged residues in CL7 with Ala, and in some cases, an amino acid with the same charge, and then investigated the effects on MRP1 expression, transport activity, and nucleotide and substrate interactions. A triple mutant in which Glu(1169), Glu(1170), and Glu(1172) were all replaced with Ala was also examined. The properties of R1173A and E1184A were comparable with those of wild-type MRP1, whereas the remaining mutants were either poorly expressed (R1166A, D1183A) or exhibited reduced transport of one or more organic anions (E1144A, D1179A, K1181A, (1169)AAQA). Same charge mutant D1183E was also not expressed, whereas expression and activity of R1166K were similar to wild-type MRP1. The moderate substrate-selective changes in transport activity displayed by mutants E1144A, D1179A, K1181A, and (1169)AAQA were accompanied by changes in orthovanadate-induced trapping of [alpha-(32)P]azidoADP by NBS2 indicating changes in ATP hydrolysis or release of ADP. In the case of E1144A, estradiol glucuronide no longer inhibited trapping of azidoADP. Together, our results demonstrate the extreme sensitivity of CL7 to mutation, consistent with its critical and complex dual role in both the proper folding and transport activity of MRP1.