Social Networks: Toward General Models Rob Stocker On Graphs, Networks, and Social Groups David Green, Terry Bossomaier Language Networks Terry Bossomaier Complexity and Human Society David Green, Susan Sadedin Developing Agent-Based Models of Business Relations and Networks Ian Wilkinson, Fabian Held, Robert Marks, Louise Young Agent-Based Modeling of Social Networks: Natural Resource Applications of Human/Landscape Interactions Across Space and Time Randy Gimblett, Robert Itami, Aaron Poe Social Media Networks and the "Unthinkable Present": A User's Perspective John Carroll, David Cameron

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. Naïve Bayes is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. K-means clustering is one of the most popular clustering techniques; however initial centroid selection strongly affects its results. This paper demonstrates the effectiveness of an unsupervised learning technique which is kmeans clustering in improving supervised learning technique which is naïve bayes. It investigates integrating K-means clustering with Naïve Bayes in the diagnosis of heart disease patients. It also investigates different methods of initial centroid selection of the K-means clustering such as range, inlier, outlier, random attribute values, and random row methods in the diagnosis of heart disease patients. The results show that integrating k-means clustering with naïve bayes with different initial centroid selection could enhance the naïve bayes accuracy in diagnosing heart disease patients. It also showed that the two clusters random row initial centroid selection method could achieve higher accuracy than other initial centroid selection methods in the diagnosis of heart disease patients showing accuracy of 84.5%.

The availability of huge amounts of medical data leads to the need for powerful data analysis tools to extract useful knowledge. Researchers have long been concerned with applying statistical and data mining tools to improve data analysis on large data sets. Disease diagnosis is one of the applications where data mining tools are proving successful results. Heart disease is the leading cause of death all over the world in the past ten years. Several researchers are using statistical and data mining tools to help health care professionals in the diagnosis of heart disease. Using single data mining technique in the diagnosis of heart disease has been comprehensively investigated showing acceptable levels of accuracy. Recently, researchers have been investigating the effect of hybridizing more than one technique showing enhanced results in the diagnosis of heart disease. However, using data mining techniques to identify a suitable treatment for heart disease patients has received less attention. This paper identifies gaps in the research on heart disease diagnosis and treatment and proposes a model to systematically close those gaps to discover if applying data mining techniques to heart disease treatment data can provide as reliable performance as that achieved in diagnosing heart disease.

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease patients. Decision Tree is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. K-means clustering is one of the most popular clustering techniques; however initial centroid selection strongly affects its results. This paper investigates integrating k-means clustering with decision tree in the diagnosis of heart disease patients. It also investigates different methods of initial centroid selection of the k-means clustering such as inlier, outlier, range, random attribute values, and random row methods in the diagnosis of heart disease patients. The results show that integrating k-means clustering with decision tree with different initial centroid selection could enhance the decision tree accuracy in the diagnosing heart disease patients. It also showed that the inlier initial centroid selection method could achieve higher accuracy than other initial centroid selection methods in the diagnosis of heart disease patients. Keywords-Data Mining, K-Means Clustering, Initial Centroid Selection Methods, Decision Tree, Heart Disease Diagnosis.

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. K-Nearest-Neighbour(KNN) is one of the successful data mining techniques used in classification problems. However, it is less used in the diagnosis of heart disease patients. Recently, researchers are showing that combining different classifiers through voting is outperforming other single classifiers. This paper investigates applying KNN to help healthcare professionals in the diagnosis of heart disease. It also investigates if integrating voting with KNN can enhance its accuracy in the diagnosis of heart disease patients. The results show that applying KNN could achieve higher accuracy than neural network ensemble in the diagnosis of heart disease patients. The results also show that applying voting could not enhance the KNN accuracy in the diagnosis of heart disease.

Review of:Mascaro, Steven, Korb, Kevin B., Nicholson, Ann E. and Woodberry, Owen (2010) Evolving Ethics: The New Science of Good and Evil. Imprint Academic: Exeter, Devon UK

The agent-based modeling paradigm has been actively applied to address social normative issues such as: values, cognition, morality and behaviors. The abstraction of human and human-like social processes and mechanisms result in misalignment of computational model with existing (old) verification and validation techniques and expose significant challenges. We argue that human sources represent a sound approach for verification and validation of agent-based social simulation models. We propose a novel conceptual gaming framework that extracts required information from relevant sources as part of game play.

In many agent-based models theoretical and computational mechanisms are needed for model abstraction and design. However, it can be challenging to arrive at the appropriate mechanisms and models. This research on the interplay of ethical trust and social moral norms addresses that challenge via an analytical framework on the spread of moral norms, the modelling of social environment and the selection of spread mechanisms as applied to agent-based social simulation. We describe the mechanism alignment mapping, two forms of interaction modelling between the social environment and agents, and the results obtained from the simulation of our computational model. These results provide an insight into how the agent-based paradigm can be applied as a technique of investigation for normative moral processes in computational social sciences.

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease. Decision Tree is one of the successful data mining techniques used. However, most research has applied J4.8 Decision Tree, based on Gain Ratio and binary discretization. Gini Index and Information Gain are two other successful types of Decision Trees that are less used in the diagnosis of heart disease. Also other discretization techniques, voting method, and reduced error pruning are known to produce more accurate Decision Trees. This research investigates applying a range of techniques to different types of Decision Trees seeking better performance in heart disease diagnosis. A widely used benchmark data set is used in this research. To evaluate the performance of the alternative Decision Trees the sensitivity, specificity, and accuracy are calculated. The research proposes a model that outperforms J4.8 Decision Tree and Bagging algorithm in the diagnosis of heart disease patients.

Social networks generally display a positively skewed degree distribution and higher values for clustering coefficient and degree assortativity than would be expected from the degree sequence. For some types of simulation studies, these properties need to be varied in the artificial networks over which simulations are to be conducted. Various algorithms to generate networks have been described in the literature but their ability to control all three of these network properties is limited. We introduce a spatially constructed algorithm that generates networks with constrained but arbitrary degree distribution, clustering coefficient and assortativity. Both a general approach and specific implementation are presented. The specific implementation is validated and used to generate networks with a constrained but broad range of property values.

The need for guiding model formulation of normative social systems in support of a digital ecosystem is introduced. Normative social systems improve the understanding of computational social processes in simulation and experimentation, and provide support for digital ecosystem developments. However, a successful simulation requires the appropriate implementation of a conceptual model. It is proposed that an heuristic formalism of agents, networks and environments, complements the conventional creative approach to model formulation by guiding the formulation of conceptual models via abstract components and facilitate interface with other components in a digital environment.

Epidemic models have successfully included many aspects of the complex contact structure apparent in real-world populations. However, it is difficult to accommodate variations in the number of contacts, clustering coefficient and assortativity. Investigations of the relationship between these properties and epidemic behaviour have led to inconsistent conclusions and have not accounted for their interrelationship. In this study, simulation is used to estimate the impact of social network structure on the probability of an SIR (susceptible-infective-removed) epidemic occurring and, if it does, the final size. Increases in assortativity and clustering coefficient are associated with smaller epidemics and the impact is cumulative. Derived values of the basic reproduction ratio (R(0)) over networks with the highest property values are more than 20% lower than those derived from simulations with zero values of these network properties.

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

The understanding of the micro-macro link is an urgent need in the study of social systems. The complex adaptive nature of social systems adds to the challenges of understanding social interactions and system feedback and presents substantial scope and potential for extending the frontiers of computer-based research tools such as simulations and agent-based technologies. In this project, we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the development of collective social moral norms as representative sample of the bigger micro-macro link of social systems. We outline our computational model of ethical trust (CMET) informed by research findings from trust, machine ethics and neural science. Guided by the CMET architecture, we discuss key implementation ideas for the simulations of ethical trust and social moral norms.

Recent advances in the fields of robotics, cyborg development, moral psychology, trust, multi agent-based systems and socionics have raised the need for a better understanding of ethics, moral reasoning, judgment and decision-making within the system of man and machines. Here we seek to understand key research questions concerning the interplay of ethical trust at the individual level and the social moral norms at the collective end. We review salient works in the fields of trust and machine ethics research, underscore the importance and the need for a deeper understanding of ethical trust at the individual level and the development of collective social moral norms. Drawing upon the recent findings from neural sciences on mirror-neuron system (MNS) and social cognition, we present a bio-inspired Computational Model of Ethical Trust (CMET) to allow investigations of the interplay of ethical trust and social moral norms.

Social groups form where individuals who are attracted to each other - usually by a common interest --- interact and form clusters. These groups exist within structural networks that rely on the patterns of links between members through which communication and resource transfer occurs. Individual influence impacts on emergent characteristics of a group, for example, global opinion and collective behaviour. However, individuals join and leave groups, thus changing the system's dynamics. What impact do these structural changes have on the emergence of sub-groups? Here our interest is in the association of members around a particular ideology and real social network systems provide our bio-inspired simulation models. We address the effects of dynamic structural changes to randomly connected networks on global behaviour and the emergence of subgroups that associate with specific states. Results from multi-agent simulations demonstrate that social cohesion and collection of nodes around particular states are dependent on group dynamics and can have an impact on social management that effects social order and stability.

One impact of the introduction of television, according to widely held views, is an undermining of traditional values and social organization. In this study, we simulate this process by representing social communication as a Random Boolean Network in which the individuals are nodes, and each node's state represents an opinion (yes/no) about some issue. Television is modelled as having a direct link to every node in the network. Two scenarios were considered. First, we found that, except in the most well connected networks, television rapidly breaks down cohesion (agreement in opinion). Second, the introduction of Hebbian learning leads to a polarizing effect : one subgroup strongly retains the original opinion, while a splinter group adopts the contrary opinion. The system displays criticality with respect to connectivity and the level of exposure to television. More generally, the results suggest that patterns of communication in networks can help to explain a wide variety of social phenomena.