Matthew J. Berryman

Methods for Generating Complex Networks with Selected Structural Properties for Simulations: A Review and Tutorial for Neuroscientists

Many simulations of networks in computational neuroscience assume completely homogenous random networks of the Erdös–Rényi type, or regular networks, despite it being recognized for some time that anatomical brain networks are more complex in their connectivity and can, for example, exhibit the “scale-free” and “small-world” properties. We review the most well known algorithms for constructing networks with given non-homogeneous statistical properties and provide simple pseudo-code for reproducing such networks in software simulations. We also review some useful mathematical results and approximations associated with the statistics that describe these network models, including degree distribution, average path length, and clustering coefficient. We demonstrate how such results can be used as partial verification and validation of implementations. Finally, we discuss a sometimes overlooked modeling choice that can be crucially important for the properties of simulated networks: that of network directedness. The most well known network algorithms produce undirected networks, and we emphasize this point by highlighting how simple adaptations can instead produce directed networks.

Scaling in small-world resistor networks

We study the effective resistance of small-world resistor networks. Utilizing recent analytic results for the propagator of the Edwards–Wilkinson process on small-world networks, we obtain the asymptotic behavior of the disorder-averaged two-point resistance in the large system-size limit. We find that the small-world structure suppresses large network resistances: both the average resistance and its standard deviation approaches a finite value in the large system-size limit for any non-zero density of random links. We also consider a scenario where the link conductance decays as a power of the length of the random links, l −α . In this case we find that the average effective system resistance diverges for any non-zero value of α.

Automated Authorship Attribution Using Advanced Signal Classification Techniques

In this paper, we develop two automated authorship attribution schemes, one based on Multiple Discriminant Analysis (MDA) and the other based on a Support Vector Machine (SVM). The classification features we exploit are based on word frequencies in the text. We adopt an approach of preprocessing each text by stripping it of all characters except a-z and space. This is in order to increase the portability of the software to different types of texts. We test the methodology on a corpus of undisputed English texts, and use leave-one-out cross validation to demonstrate classification accuracies in excess of 90%. We further test our methods on the Federalist Papers, which have a partly disputed authorship and a fair degree of scholarly consensus. And finally, we apply our methodology to the question of the authorship of the Letter to the Hebrews by comparing it against a number of original Greek texts of known authorship. These tests identify where some of the limitations lie, motivating a number of open questions for future work. An open source implementation of our methodology is freely available for use at https://github.com/matthewberryman/author-detection.

Advanced text authorship detection methods and their application to biblical texts

Authorship attribution has a range of applications in a growing number of fields such as forensic evidence, plagiarism detection, email filtering, and web information management. In this study, three attribution techniques are extended, tested on a corpus of English texts, and applied to a book in the New Testament of disputed authorship. The word recurrence interval based method compares standard deviations of the number of words between successive occurrences of a keyword both graphically and with chi-squared tests. The trigram Markov method compares the probabilities of the occurrence of words conditional on the preceding two words to determine the similarity between texts. The third method extracts stylometric measures such as the frequency of occurrence of function words and from these constructs text classification models using multiple discriminant analysis. The effectiveness of these techniques is compared. The accuracy of the results obtained by some of these extended methods is higher than many of the current state of the art approaches. Statistical evidence is presented about the authorship of the selected book from the New Testament.

MUTUAL INFORMATION FOR EXAMINING CORRELATIONS IN DNA

This paper examines two methods for finding whether long-range correlations exist in DNA: a fractal measure and a mutual information technique. We evaluate the performance and implications of these...

Tutorials on agent-based modelling with NetLogo and network analysis with Pajek

Complex adaptive systems typically contain multiple, heterogeneous agents, with non-trivial interactions. They tend to produce emergent (larger-scale) phenomena. Agent-based modelling allows one to readily capture the behaviour of a group of heterogeneous agents (such as people, animals, et cetera), with diverse behaviour and important interactions, so it is a natural t to modelling complex systems. Many complex systems (and agent-based models thereof) can be thought of as containing networks, either explicitly or implicitly. Therefore for complex systems research it is important to have a good understanding of network analysis techniques. This chapter is aimed at beginners to complex systems modelling and network analysis, using NetLogo (Section 1.1) and Pajek (Section 1.2) respectively. It is also aimed at more advanced complex systems modellers who want an introduction to these platforms.

STATISTICAL TECHNIQUES FOR TEXT CLASSIFICATION BASED ON WORD RECURRENCE INTERVALS

We present a method for characterizing text based on a statistical analysis of word recurrence interval. This method can be used for extracting keywords from text, and also for comparing texts by an unknown author against a set of known authors. We also use these methods to comment on the controversial question of who wrote the letter to the Hebrews in the New Testament.

Using geospatial business intelligence to support regional infrastructure governance

In many developed countries including Australia, infrastructure services at local and state levels are being provided by an increasing number of disjointed public and private agencies. There is an urgent need for an integrated view on the provision and use of these services for better governance and productivity. Developing an integrated view is challenging due to the dispersion of relevant data sets and the underlying complexity of increasingly interconnected infrastructure networks. Using a case study in New South Wales (Australia), we demonstrate how tools and processes in Geospatial Business Intelligence (Geo-BI) can be harnessed using a collective design approach to develop an integrated solution; the SMART Infrastructure Dashboard (SID). While providing a much needed planning and policy support tool for the local governance of infrastructure services, SID pushes the boundary of Geo-BI beyond its traditional use.

Answering human papillomavirus vaccine concerns; a matter of science and time.

Since the introduction of the HPV vaccine, questions have been asked about its efficacy in preventing cancer linked with HPV. Concerns about the HPV vaccine safety profile have also been raised. This paper highlights the rapidly growing body of evidence (including clinical trials and post-marketing surveillance) illustrating both the safety of the HPV vaccine, through a detailed investigation of reported adverse events, and its efficacy in reducing both HPV infections rates and the resulting drop in cervical lesions, which have been demonstrated to be good predictors of cervical cancer risk.

Simulating Transport and Land Use Interdependencies for Strategic Urban Planning—An Agent Based Modelling Approach

Agent based modelling has been widely accepted as a promising tool for urban planning purposes thanks to its capability to provide sophisticated insights into the social behaviours and the interdependencies that characterise urban systems. In this paper, we report on an agent based model, called TransMob, which explicitly simulates the mutual dynamics between demographic evolution, transport demands, housing needs and the eventual change in the average satisfaction of the residents of an urban area. The ability to reproduce such dynamics is a unique feature that has not been found in many of the like agent based models in the literature. TransMob, is constituted by six major modules: synthetic population, perceived liveability, travel diary assignment, traffic micro-simulator, residential location choice, and travel mode choice. TransMob is used to simulate the dynamics of a metropolitan area in South East of Sydney, Australia, in 2006 and 2011, with demographic evolution. The results are favourably compared against survey data for the area in 2011, therefore validating the capability of TransMob to reproduce the observed complexity of an urban area. We also report on the application of TransMob to simulate various hypothetical scenarios of urban planning policies. We conclude with discussions on current limitations of TransMob, which serve as suggestions for future developments.