Applications of Structural Balance in Signed Social Networks

We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual 'friend' links, 'enemy' links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be 'balanced' towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.Comment: 37 page

Consumer behavior, social influence, and smart grid implementation

To achieve the goals of German energy transition especially in renewable energy shares, the smart grid will play a key role in managing the demand able to match more volatile supply and optimizing the entire electricity system. Even though the system transformation is technically feasible, the successful transition cannot live without end users willing to transform their way of using energy. This thesis has explored possible roles of individual consumers in the smart grid implementation and in detail analyzed their influential factors. An online survey was conducted to capture preferences and behaviors of energy consumers during the time period of November 2013 to January 2014. The three roles of private electricity consumers - as consumers consuming electricity through appliances, as citizens holding attitudes towards smart grid applications, and as potential producers of electricity - are targeted. Constructs from the theory of planned behavior were tested by using a sample of 517 German citizens. Structural equation models of individual’s electricity saving behavior, their intention to participate in smart grid applications and investment behavior in solar panels were built. It was found that determinants of attitude, perceived norm, and perceived behavioral control together explain 32%-56% of the variance in the three behaviors. Attitude was found to be the most influential factor of individual electricity saving behavior, as well as of citizens’ intentions to participate in smart grid applications. For solar panel investment, it is perceived behavioral control that has the highest impact on the behavior. As the smart grid concept is not well understood by common people, education program and information campaigns are needed, in which social norm marketing is worth more attention, ascribable to the considerable impact caused by the diffusion of norms through social networks. To examine this social influence effect, empirically founded agent-based models for the above-mentioned three behaviors were created to estimate possible behavior changes brought by social norms at the aggregate level. Simulation results show that a reduction of total consumptions by 20% could be achieved in the virtual community due to behavior conformity induced by identified adopters. The potential impact of social norms on home generation and load shift are also promising

Investigating social networks with Agent Based Simulation and Link Prediction methods

Social networks are increasingly being investigated in the context of individual behaviours. Research suggests that friendship connections have the ability to influence individual actions, change personal opinions and subsequently impact upon personal wellbeing. This thesis aims to investigate the effects of social networks, through the use of Agent Based Simulation (ABS) and Link Prediction (LP) methods. Three main investigations form this thesis, culminating in the development of a new simulation-based approach to Link Prediction (PageRank-Max) and a model of behavioural spread through a connected population (Behavioural PageRank-Max). The first project investigates the suitability of ABS to explore a connected social system. The Peter Principle is a theory of managerial incompetence, having the potential to cause detrimental effects to system efficiency. Through the investigation of a theoretical hierarchy of workplace social contacts, it is observed that the structure of a social network has the ability to impact system efficiency, demonstrating the importance of social network structure in conjunction with individual behaviours. The second project aims to further understand the structure of social networks, through the exploration of adolescent offline friendship data, taken from 'A Stop Smoking in Schools Trial' (ASSIST). An initial analysis of the data suggests certain factors may be pertinent in the formation of school social networks, identifying the importance of centrality measures. An ABS aiming to predict the evolution of the ASSIST social networks is created, developing an algorithm based upon the optimisation of an individual's eigen-centrality - termed PageRank-Max. This new approach to Link Prediction is found to predict ASSIST social network evolution more accurately than four existing prominent LP algorithms. The final part of this thesis attempts to improve the PageRank-Max method, by placing particular emphasis upon specific individual attributes. Two new methods are developed, the first restricting the search space of the algorithm (Behavioural Search), while the second alters its calculation process by applying specific attribute weights (Behavioural PageRank-Max). The results demonstrate the importance of individual attributes in adolescent friendship selection. Furthermore, the Behavioural PageRank-Max offers an approach to model the spread of behaviours in conjunction with social network structure, with the value of this being evaluated against alternative models