Multifractal characterisation and analysis of complex networks

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this thesis, we propose a new box covering algorithm for multifractal analysis of complex networks. This algorithm is demonstrated in the computation of the generalized fractal dimensions of some theoretical networks, namely scale-free networks, small-world networks, random networks, and a kind of real networks, namely PPI networks of different species. Our main finding is the existence of multifractality in scale-free networks and PPI networks, while the multifractal behaviour is not confirmed for small-world networks and random networks. As another application, we generate gene interactions networks for patients and healthy people using the correlation coefficients between microarrays of different genes. Our results confirm the existence of multifractality in gene interactions networks. This multifractal analysis then provides a potentially useful tool for gene clustering and identification. The third part of the thesis aims to investigate the topological properties of networks constructed from time series. Characterizing complicated dynamics from time series is a fundamental problem of continuing interest in a wide variety of fields. Recent works indicate that complex network theory can be a powerful tool to analyse time series. Many existing methods for transforming time series into complex networks share a common feature: they define the connectivity of a complex network by the mutual proximity of different parts (e.g., individual states, state vectors, or cycles) of a single trajectory. In this thesis, we propose a new method to construct networks of time series: we define nodes by vectors of a certain length in the time series, and weight of edges between any two nodes by the Euclidean distance between the corresponding two vectors. We apply this method to build networks for fractional Brownian motions, whose long-range dependence is characterised by their Hurst exponent. We verify the validity of this method by showing that time series with stronger correlation, hence larger Hurst exponent, tend to have smaller fractal dimension, hence smoother sample paths. We then construct networks via the technique of horizontal visibility graph (HVG), which has been widely used recently. We confirm a known linear relationship between the Hurst exponent of fractional Brownian motion and the fractal dimension of the corresponding HVG network. In the first application, we apply our newly developed box-covering algorithm to calculate the generalized fractal dimensions of the HVG networks of fractional Brownian motions as well as those for binomial cascades and five bacterial genomes. The results confirm the monoscaling of fractional Brownian motion and the multifractality of the rest. As an additional application, we discuss the resilience of networks constructed from time series via two different approaches: visibility graph and horizontal visibility graph. Our finding is that the degree distribution of VG networks of fractional Brownian motions is scale-free (i.e., having a power law) meaning that one needs to destroy a large percentage of nodes before the network collapses into isolated parts; while for HVG networks of fractional Brownian motions, the degree distribution has exponential tails, implying that HVG networks would not survive the same kind of attack.

Keeping friends safe : a prospective study examining early adolescent's confidence and support networks

There is a continued need to consider ways to prevent early adolescent engagement in a variety of harmful risk-taking behaviours for example, violence, road-related risks and alcohol use. The current prospective study examined adolescents’ reports of intervening to try and stop friends’ engagement in such behaviours among 207 early adolescents (mean age = 13.51 years, 50.1% females). Findings showed that intervening behaviour after three months was predicted by the confidence to intervene which in turn was predicted by student and teacher support although not parental support. The findings suggest that the benefits of positive relationship experiences might extend to the safety of early adolescent friendship groups particularly through the development of confidence to try and stop friends’ risky and dangerous behaviours. Findings from the study support the important role of the school in creating a culture of positive adolescent behaviour whereby young people take social responsibility.

Problematising the processes of participation in networks : working through the rhetoric

Participation in networks, both as a concept and process, is widely supported in environmental education as a democratic and equitable pathway to individual and social change for sustainability. However, the processes of participation in networks are rarely problematized. Rather, it is assumed that we inherently know how to participate in networks. This assumption means that participation is seldom questioned. Underlying support for participation in networks is a belief that it allows individuals to connect in new and meaningful ways, that individuals can engage in making decisions and in bringing about change in arenas that affect them, and that they will be engaging in new, non-hierarchical and equitable relationships. In this paper we problematize participation in networks. As an example we use research into a decentralized network – described as such in its own literature - the Queensland Environmentally Sustainable Schools Initiative Alliance in Australia – to argue that while network participants were engaged and committed to participation in this network, 'old' forms of top-down engagement and relationships needed to be unlearnt. This paper thus proposes that for participation in decentralized networks to be meaningful, new learning about how to participate needs to occur.

Structural damage detection using frequency response functions and neural networks

This paper illustrates the damage identification and condition assessment of a three story bookshelf structure using a new frequency response functions (FRFs) based damage index and Artificial Neural Networks (ANNs). A major obstacle of using measured frequency response function data is a large size input variables to ANNs. This problem is overcome by applying a data reduction technique called principal component analysis (PCA). In the proposed procedure, ANNs with their powerful pattern recognition and classification ability were used to extract damage information such as damage locations and severities from measured FRFs. Therefore, simple neural network models are developed, trained by Back Propagation (BP), to associate the FRFs with the damage or undamaged locations and severity of the damage of the structure. Finally, the effectiveness of the proposed method is illustrated and validated by using the real data provided by the Los Alamos National Laboratory, USA. The illustrated results show that the PCA based artificial Neural Network method is suitable and effective for damage identification and condition assessment of building structures. In addition, it is clearly demonstrated that the accuracy of proposed damage detection method can also be improved by increasing number of baseline datasets and number of principal components of the baseline dataset.

Metaheuristics for minimizing the makespan of the dynamic shop scheduling problem

For the shop scheduling problems such as flow-shop, job-shop, open-shop, mixed-shop, and group-shop, most research focuses on optimizing the makespan under static conditions and does not take into consideration dynamic disturbances such as machine breakdown and new job arrivals. We regard the shop scheduling problem under static conditions as the static shop scheduling problem, while the shop scheduling problem with dynamic disturbances as the dynamic shop scheduling problem. In this paper, we analyze the characteristics of the dynamic shop scheduling problem when machine breakdown and new job arrivals occur, and present a framework to model the dynamic shop scheduling problem as a static group-shop-type scheduling problem. Using the proposed framework, we apply a metaheuristic proposed for solving the static shop scheduling problem to a number of dynamic shop scheduling benchmark problems. The results show that the metaheuristic methodology which has been successfully applied to the static shop scheduling problems can also be applied to solve the dynamic shop scheduling problem efficiently.

Internet-enabled international entrepreneurship : a conceptual exploration of the link between a firm’s resource base, dynamic capabilities and international market performance

It has been suggested that the accumulation of valuable resources and capabilities, such as Internet application, is not enough to support a firm’s sustainable competitive advantage, especially for high technology-mediated firms; which often operate in fast changing dynamic environments. While the idea of ‘Internet-enabled resources and capabilities’ has been recognised by RBV theorists, the notion has largely been ignored in conceptual and empirical studies. Given this finding, a conceptual framework is constructed and research issues are then developed in order to focus attention on the relationship between, the Internet and a firm’s resource base, dynamic capabilities and international market performance. We postulate that successful Internet-enabled market performance arises from those international entrepreneurial-oriented firms which encompass: international vision, international business experience, Internet-international marketing capabilities and international networking capabilities. Recommendations for future theory development are presented, along with the implications for international entrepreneurial managers in Australian small and medium sized firms

Microstrip decoupling networks for low-order multi-port arrays with reduced element spacing

An array of monopole elements with reduced element spacing of λ/6 to λ/20 is considered for application in digital beam-forming and direction-finding. The small element spacing introduces strong mutual coupling between the array elements. This paper discusses that decoupling can be achieved analytically for arrays with three elements and describes Kuroda’s identities to realize the lumped elements of the derived decoupling network. Design procedures and equations are proposed. Experimental results are presented. The decoupled array has a bandwidth of 1% and a superdirective radiation pattern.

Diversified service provisioning in global business networks

Companies and their services are being increasingly exposed to global business networks and Internet-based ondemand services. Much of the focus is on flexible orchestration and consumption of services, beyond ownership and operational boundaries of services. However, ways in which third-parties in the “global village” can seamlessly self-create new offers out of existing services remains open. This paper proposes a framework for service provisioning in global business networks that allows an open-ended set of techniques for extending services through a rich, multi-tooling environment. The Service Provisioning Management Framework, as such, supports different modeling techniques, through supportive tools, allowing different parts of services to be integrated into new contexts. Integration of service user interfaces, business processes, operational interfaces and business object are supported. The integration specifications that arise from service extensions are uniformly reflected through a kernel technique, the Service Integration Technique. Thus, the framework preserves coherence of service provisioning tasks without constraining the modeling techniques needed for extending different aspects of services.

Human seat modelling using inverse dynamic musculoskeletal models

In this work a biomechanical model is used for simulation of muscle forces necessary to maintain the posture in a car seat under different support conditions.

Text mining and probabilistic language modeling for online review spam detecting

In the era of Web 2.0, huge volumes of consumer reviews are posted to the Internet every day. Manual approaches to detecting and analyzing fake reviews (i.e., spam) are not practical due to the problem of information overload. However, the design and development of automated methods of detecting fake reviews is a challenging research problem. The main reason is that fake reviews are specifically composed to mislead readers, so they may appear the same as legitimate reviews (i.e., ham). As a result, discriminatory features that would enable individual reviews to be classified as spam or ham may not be available. Guided by the design science research methodology, the main contribution of this study is the design and instantiation of novel computational models for detecting fake reviews. In particular, a novel text mining model is developed and integrated into a semantic language model for the detection of untruthful reviews. The models are then evaluated based on a real-world dataset collected from amazon.com. The results of our experiments confirm that the proposed models outperform other well-known baseline models in detecting fake reviews. To the best of our knowledge, the work discussed in this article represents the first successful attempt to apply text mining methods and semantic language models to the detection of fake consumer reviews. A managerial implication of our research is that firms can apply our design artifacts to monitor online consumer reviews to develop effective marketing or product design strategies based on genuine consumer feedback posted to the Internet.

Impact of embeddedness in ethnic networks on business growth : evidence from Chinese immigrant entrepreneurs in Australia

Immigrant entrepreneurship, or, self-employment by immigrants (Light & Bonacich, 1988), has been of growing interest to researchers (Hosler, 1996). This is due in part to major immigrant receiving countries, such as Australia, the United States, Canada, the United Kingdom and Western Europe, experiencing a high growth rate in their immigrant populations, leading to a more visible presence of immigrant business in major cities (Woon, 2008). By starting their own businesses, immigrant entrepreneurs may circumvent some of the barriers and disadvantages encountered in looking for a job (Sequeira & Rasheed, 2006). Successful immigrant entrepreneurs will integrate into the economy by creating jobs, providing products and services for members of their own ethnic community and society, as well as introducing new products and services that expand consumers’ choices (Rath & Kloosterman, 2000). Immigrant entrepreneurs tend to start business within their ethnic enclave, as it is an integral part of their social and cultural context and the location where ethnic resources reside (Logan et al., 2002). An ethnic enclave is an interdependent network of social and business relationships that are geographically concentrated with its co-ethnic people (Portes & Bach, 1985).

Probabilistic matching of image sets for video-based face recognition

We address the problem of face recognition on video by employing the recently proposed probabilistic linear discrimi-nant analysis (PLDA). The PLDA has been shown to be robust against pose and expression in image-based face recognition. In this research, the method is extended and applied to video where image set to image set matching is performed. We investigate two approaches of computing similarities between image sets using the PLDA: the closest pair approach and the holistic sets approach. To better model face appearances in video, we also propose the heteroscedastic version of the PLDA which learns the within-class covariance of each individual separately. Our experi-ments on the VidTIMIT and Honda datasets show that the combination of the heteroscedastic PLDA and the closest pair approach achieves the best performance.

On the application of the probabilistic linear discriminant analysis to face recognition across expression

Facial expression is one of the main issues of face recognition in uncontrolled environments. In this paper, we apply the probabilistic linear discriminant analysis (PLDA) method to recognize faces across expressions. Several PLDA approaches are tested and cross-evaluated on the Cohn-Kanade and JAFFE databases. With less samples per gallery subject, high recognition rates comparable to previous works have been achieved indicating the robustness of the approaches. Among the approaches, the mixture of PLDAs has demonstrated better performances. The experimental results also indicate that facial regions around the cheeks, eyes, and eyebrows are more discriminative than regions around the mouth, jaw, chin, and nose.

Book Review: "Mark Considine, Jenny Lewis and Damon Alexander Networks, Innovation and Public Policy : Politicians, Bureaucrats and the Pathways to Change inside Government Houndsmills, UK: Palgrave Macmillan, 2009"

Having wrung the most from workforce and workplace productivity initiaitves, innovation has come to the fore as a key goal and directive for public sector organisations to become more efficient. This clarion call for innovation can be heard all around the world, with public services everywhere taking up the message to develop better, smarter, novel, more innovative processes, programs and policies. In the current push for innovation, networks are considered to be a superior vehicle through which collective knowledge can be shared and leveraged; replacing or at least supplementing the role function previously provided by inventive leaders...