Entropy Based Sensitivity Analysis and Visualization of Social Networks

Abstract. This paper introduces a technique to analyze and visualize a social network using Shannon's entropy model. We used degree entropy and presented novel measures such as, betweenness and closeness entropies to conduct network sensitivity analysis by means of evaluating the change of graph entropy via those measures. We integrated the result of our analyses into a visualization application where the social network is presented using node-link diagram. The size of visual representation of an actor depends on the amount of change in system entropy caused by the actor and color information is extracted from the graph clustering analysis. Filtering of edges and nodes is also provided to enable and improve the perception of complex graphs. The main contribution is that the information communicated from a social network is enhanced by means of sensitivity analyses and visualization techniques provided with this work

Using global diversity and local topology features to identify influential network spreaders

AbstractIdentifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible–Infected–Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets

Statistical L-moment and L-moment Ratio Estimation and their Applicability in Network Analysis

This research centers on finding the statistical moments, network measures, and statistical tests that are most sensitive to various node degradations for the Barabási-Albert, Erdös-Rényi, and Watts-Strogratz network models. Thirty-five different graph structures were simulated for each of the random graph generation algorithms, and sensitivity analysis was undertaken on three different network measures: degree, betweenness, and closeness. In an effort to find the statistical moments that are the most sensitive to degradation within each network, four traditional moments: mean, variance, skewness, and kurtosis as well as three non-traditional moments: L-variance, L-skewness, and L-kurtosis were examined. Each of these moments were examined across 18 degrade settings to highlight which moments were able to detect node degradation the quickest. Closeness and the mean were the most sensitive measures to node degradation across all scenarios. The results showed L-moments and L-moment ratios were less sensitive than traditional moments. Subsequently sample size guidance and confidence interval estimation for univariate and joint L-moments were derived across many common statistical distributions for future research with L-moments

Detection of illicit behaviours and mining for contrast patterns

This thesis describes a set of novel algorithms and models designed to detect illicit behaviour. This includes development of domain specific solutions, focusing on anti-money laundering and detection of opinion spam. In addition, advancements are presented for the mining and application of contrast patterns, which are a useful tool for characterising illicit behaviour. For anti-money laundering, this thesis presents a novel approach for detection based on analysis of financial networks and supervised learning. This includes the development of a network model, features extracted from this model, and evaluation of classifiers trained using real financial data. Results indicate that this approach successfully identifies suspicious groups whose collaborative behaviour is indicative of money laundering. For the detection of opinion spam, this thesis presents a model of reviewer behaviour and a method for detection based on statistical anomaly detection. This method considers review ratings, and does not rely on text-based features. Evaluation using real data shows that spammers are successfully identified. Comparison with existing methods shows a small improvement in accuracy, but significant improvements in computational efficiency. This thesis also considers the application of contrast patterns to network analysis and presents a novel algorithm for mining contrast patterns in a distributed system. Contrast patterns may be used to characterise illicit behaviour by contrasting illicit and non-illicit behaviour and uncovering significant differences. However, existing mining algorithms are limited by serial processing making them unsuitable for large data sets. This thesis advances the current state-of-the-art, describing an algorithm for mining in parallel. This algorithm is evaluated using real data and is shown to achieve a high level of scalability, allowing mining of large, high-dimensional data sets. In addition, this thesis explores methods for mapping network features to an item-space suitable for analysis using contrast patterns. Experiments indicate that contrast patterns may become a valuable tool for network analysis

Entropy based sensitivity analysis and visualization of social networks

This paper introduces a technique to analyze and visualize social networks using Shannon's entropy model. Entropy is exploited to measure the information amount in social network graphs, and to conduct sensitivity analyses. Novel measures such as degree, betweenness and closeness entropies are evaluated to find the change in graph entropy for the actors. In this work we present a visualization approach that uses coloring, sizing and filtering to help the users perceive the communicated information. The result of sensitivity analyses is integrated into the visualization using the change amount caused by the actors as information. The main contribution of this study is a visualization where the information communicated from a social network is enhanced by the help of sensitivity analyses