Correlation Dimension of Complex Networks

We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers.

An algorithm for controllability in complex networks

In this paper structural controllability of complex networks is anyzed. A new algorithm is proposed which constructs a structural control scheme for a given network by avoiding the absence of dilations and by guaranteeing the accessibility of all nodes. Such accessibility is solved via a wiring procedure; this procedure, based on determining the non-accessible regions of the network, has been improved in this new proposed algorithm. This way, the number of dedicated controllers is reduced with respect to the one provided by previous existing algorithms.

A combined algorithm for analyzing structural controllability and observability of complex networks

In this paper a combined algorithm for analyzing structural controllability and observability of complex networks is presented. The algorithm addresses the two fundamental properties to guarantee structural controllability of a system: the absence of dilations and the accessibility of all nodes. The first problem is reformulated as a Maximum Matching search and it is addressed via the Hopcroft- Karp algorithm; the second problem is solved via a new wiring algorithm. Both algorithms can be combined to efficiently determine the number of required controllers and observers as well as the new required connections in order to guarantee controllability and observability in real complex networks. An application to a Twitter social network with over 100,000 nodes illustrates the proposed algorithms.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

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.

Scaling and Multiscaling Exponents in Networks and Flows

The main focus of this paper is on mathematical theory and methods which have a direct bearing on problems involving multiscale phenomena. Modern technology is refining measurement and data collection to spatio-temporal scales on which observed geophysical phenomena are displayed as intrinsically highly variable and intermittant heirarchical structures,e.g. rainfall, turbulence, etc. The heirarchical structure is reflected in the occurence of a natural separation of scales which collectively manifest at some basic unit scale. Thus proper data analysis and inference require a mathematical framework which couples the variability over multiple decades of scale in which basic theoretical benchmarks can be identified and calculated. This continues the main theme of the research in this area of applied probability over the past twenty years.

Critical Exponents of Semilinear Equations via the Feynman-Kac Formula

2000 Mathematics Subject Classification: 60H30, 35K55, 35K57, 35B35.

Control efficacy of complex networks

W.-X.W. was supported by NSFC under Grant No. 11105011, CNNSF under Grant No. 61074116 and the Fundamental Research Funds for the Central Universities. Y.-C.L. was supported by ARO under Grant W911NF-14-1-0504

Reconstructing complex networks with binary-state dynamics

For Supplementary Information, see http://sss.bnu.edu.cn/~wenxuw/publications/SI_reconstruct_binary.pdf

Assessment of the EEE-4 oral test: a discourse analysis based on complex networks.

With the development of information technology, the theory and methodology of complex network has been introduced to the language research, which transforms the system of language in a complex networks composed of nodes and edges for the quantitative analysis about the language structure. The development of dependency grammar provides theoretical support for the construction of a treebank corpus, making possible a statistic analysis of complex networks. This paper introduces the theory and methodology of the complex network and builds dependency syntactic networks based on the treebank of speeches from the EEE-4 oral test. According to the analysis of the overall characteristics of the networks, including the number of edges, the number of the nodes, the average degree, the average path length, the network centrality and the degree distribution, it aims to find in the networks potential difference and similarity between various grades of speaking performance. Through clustering analysis, this research intends to prove the network parameters’ discriminating feature and provide potential reference for scoring speaking performance.

Management accounting and supply chains: actions, concerns, and networks

What does this thesis do? This thesis uses Actor-Network Theory (ANT) to examine how a UK retailer’s organization and strategy, and, in turn, its form of management accounting was shaped by its supply chain. The thesis does this by reporting on four related themes in the form of four inter-connected essays. The first essay undertakes a state-of-the-art review of the literature. It examines how accounting issues within supply chains permeate ‘matters of concern’. In accordance with this idea of ANT, the essay illustrates how issues emerged, controversies developed, and matters evolved through an actor-network of accounting researchers within the supply chain domain. This leads on to the second essay, which exemplifies the nature of the UK’s retailing industry within which the supply chain case organization emerged and developed. The purposes of the essay are twofold: to introduce the contextual ramifications of the case organization; and to illustrate the emergence of a new market logic, which led to the creation of a global supply chain and a new form of management accounting therein. The third essay reports on a qualitative case study. It analyses the dualistic relation between ostensive and performative aspects of supply chain strategy, reveals how accounting numbers act as an obligatory passage point within this dualism, and makes a contribution to the ANT debate around the issue of whether and how a dualism between ostensive and performative aspects exists. The final essay reports on another case analysis of institutionalizing a heterarchical form of management accounting: a distributed form of intelligence that penetrates through lateral accountable relations. The analysis reveals a new form of management accounting characterised by ambiguity; it emphasizes the possibilities of compromises and negotiations, and it thus contributes to knowledge by combining an aspect of ANT with heterarchical tendencies in the world of contemporary organizations. Finally, the thesis concludes that it is the supply chain that organises today’s neoliberal capitalism; and it is management accounting that unites both human and non-human actors within such supply chains, despite that form of management accounting being ambiguous. The thesis comprises the introduction, these four essays, and the conclusion.

Data surveillance for infectious diseases: models, complex networks and machine learning

In this thesis, we investigate the role of applied physics in epidemiological surveillance through the application of mathematical models, network science and machine learning. The spread of a communicable disease depends on many biological, social, and health factors. The large masses of data available make it possible, on the one hand, to monitor the evolution and spread of pathogenic organisms; on the other hand, to study the behavior of people, their opinions and habits. Presented here are three lines of research in which an attempt was made to solve real epidemiological problems through data analysis and the use of statistical and mathematical models. In Chapter 1, we applied language-inspired Deep Learning models to transform influenza protein sequences into vectors encoding their information content. We then attempted to reconstruct the antigenic properties of different viral strains using regression models and to identify the mutations responsible for vaccine escape. In Chapter 2, we constructed a compartmental model to describe the spread of a bacterium within a hospital ward. The model was informed and validated on time series of clinical measurements, and a sensitivity analysis was used to assess the impact of different control measures. Finally (Chapter 3) we reconstructed the network of retweets among COVID-19 themed Twitter users in the early months of the SARS-CoV-2 pandemic. By means of community detection algorithms and centrality measures, we characterized users’ attention shifts in the network, showing that scientific communities, initially the most retweeted, lost influence over time to national political communities. In the Conclusion, we highlighted the importance of the work done in light of the main contemporary challenges for epidemiological surveillance. In particular, we present reflections on the importance of nowcasting and forecasting, the relationship between data and scientific research, and the need to unite the different scales of epidemiological surveillance.

One archive, many readings. Personal archives as complex networks in the semantic web

Personal archives are the archives created by individuals for their own purposes. Among these are the library and documentary collections of writers and scholars. It is only recently that archival literature has begun to focus on this category of archives, emphasising how their heterogeneous nature necessitates the conciliation of different approaches to archival description, and calling for a broader understanding of the principle of provenance, recognising that multiple creators, including subsequent researchers, can contribute to shaping personal archives over time by adding new layers of contexts. Despite these advances in the theoretical debate, current architectures for archival representation remain behind. Finding aids privilege a single point of view and do not allow subsequent users to embed their own, potentially conflicting, readings. Using semantic web technologies this study aims to define a conceptual model for writers' archives based on existing and widely adopted models in the cultural heritage and humanities domains. The model developed can be used to represent different types of documents at various levels of analysis, as well as record content and components. It also enables the representation of complex relationships and the incorporation of additional layers of interpretation into the finding aid, transforming it from a static search tool into a dynamic research platform.  The personal archive and library of Giuseppe Raimondi serves as a case study for the creation of an archival knowledge base using the proposed conceptual model. By querying the knowledge graph through SPARQL, the effectiveness of the model is evaluated. The results demonstrate that the model addresses the primary representation challenges identified in archival literature, from both a technological and methodological standpoint. The ultimate goal is to bring the output par excellence of archival science, i.e. the finding aid, more in line with the latest developments in archival thinking.