Social relationship based routing for delay tolerant Bluetooth-enabled PSN communications

PhDOpportunistic networking is a concept derived from the mobile ad hoc networking in which devices have no prior knowledge of routes to the intended destinations. Content dissemination in opportunistic networks thus is carried out in a store and forward fashion. Opportunistic routing poses distinct challenges compared to the traditional networks such as Internet and mobile ad hoc networks where nodes have prior knowledge of the routes to the intended destinations. Information dissemination in opportunistic networks requires dealing with intermittent connectivity, variable delays, short connection durations and dynamic topology. Addressing these challenges becomes a significant motivation for developing novel applications and protocols for information dissemination in opportunistic networks. This research looks at opportunistic networking, specifically at networks composed of mobile devices or, pocket switched networks. Mobile devices are now accepted as an integral part of society and are often equipped with Bluetooth capabilities that allow for opportunistic information sharing between devices. The ad hoc nature of opportunistic networks means nodes have no advance routing knowledge and this is key challenge. Human social relationships are based on certain patterns that can be exploited to make opportunistic routing decisions. Targeting nodes that evidence high popularity or high influence can enable more efficient content dissemination. Based on this observation, a novel impact based neighbourhood algorithm called Lobby Influence is presented. The algorithm is tested against two previously proposed algorithms and proves better in terms of message delivery and delay. Moreover, unlike other social based algorithms, which have a tendency to concentrate traffic through their identified routing nodes, the new algorithm provides a fairer load distribution, thus alleviating the tendency to saturate individual nodes

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

Catalyzing Change in Higher Education: Social Capital and Network Leadership in the Competency-Based Education Network

Collaborative inter-organizational networks can be effective at catalyzing and supporting the generation and diffusion of new models and practices. With shared purpose, structure, and resources, network organizations can facilitate knowledge exchange and the growth of inter-organizational relationships. In this study, I sought to better understand how network organizations influence social capital and the spread of innovative practices. Of particular interest were the roles of national network and sub-national network organizations (sub-networks), and the interactive learning processes of network newcomers. I focused on the diverse array of colleges and universities involved in the Competency-Based Education Network (C-BEN), and their efforts to transform higher education practice and policy. Specific research questions were tackled to understand: (a) the dimensions of key collaborative relationships (KCRs) and their associations to outcomes; (b) the competency-based education (CBE) ecosystem’s network structure, important clusters of network activity, and key individual and organizational actors; (c) the association between KCRs and the implementation of similar CBE practices; (d) the organizational and individual factors associated with the formation of inter-organizational KCRs; and, (e) the experiences of HEIs new-to-CBE as they learn about CBE, and then design and implement new programs. A mixed methods sequential explanatory research design was employed using social network analysis and qualitative case methods. Study data was drawn from multiple sources, to include the study CBE Social Network Survey (CBESNS), a confidential American Institutes for Research survey, and from 36 semi-structured interviews. Results confirmed that strong ties and trust were important to tacit knowledge transfer and organizational innovation, and a strong correlation was found between inter-organizational collaborative work and trust. Immersive problem-solving programs were found effective for growing trust and strong relations among diverse stakeholders, along with advancing innovations in policy and practice. Lastly, a bifurcated learning process was seen for newcomers based on their potential affiliation to sub-network organizations, which connected them with impactful proximal influencers, among other benefits. Contributions to the literature are made with findings that have both theoretical and practical implications. They also anchor a research agenda for understanding how transformation can be enacted in complex systems and sectors through networks

Methods and applications in social networks analysis

The Social Network Analysis perspective has proven the ability to develop a significant breadth of theoretical and methodological issues witnessed by the contribution of an increasing number of scholars and the multiplication of empirical applications in a wide range of scientific fields. One of the disciplinary areas in which this development has occurred, among others, is certainly that of computational social science, by virtue of the developing field of online social networks and the leading role of information technologies in the production of scientific knowledge. The complex nature of social phenomena enforced the usefulness of the network perspective as a wealth of theoretical and methodological tools capable of penetrating within the dimensions of that complexity. The book hosts eleven contributions that within a sound theoretical ground, present different examples of speculative and applicative areas where the Social Network Analysis can contribute to explore, interpret and predict social interaction between actors. Some of the contributions were presented at the ARS’19 Conference held in Vietri sul Mare (Salerno, Italy) in October, 29-31 2019; it was the seventh of a biennial meetings series started in 2007 with the aim to promote relevant results and the most recent methodological developments in Social Network Analysis

Graph based Anomaly Detection and Description: A Survey

Detecting anomalies in data is a vital task, with numerous high-impact applications in areas such as security, finance, health care, and law enforcement. While numerous techniques have been developed in past years for spotting outliers and anomalies in unstructured collections of multi-dimensional points, with graph data becoming ubiquitous, techniques for structured graph data have been of focus recently. As objects in graphs have long-range correlations, a suite of novel technology has been developed for anomaly detection in graph data. This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs. As a key contribution, we give a general framework for the algorithms categorized under various settings: unsupervised vs. (semi-)supervised approaches, for static vs. dynamic graphs, for attributed vs. plain graphs. We highlight the effectiveness, scalability, generality, and robustness aspects of the methods. What is more, we stress the importance of anomaly attribution and highlight the major techniques that facilitate digging out the root cause, or the ‘why’, of the detected anomalies for further analysis and sense-making. Finally, we present several real-world applications of graph-based anomaly detection in diverse domains, including financial, auction, computer traffic, and social networks. We conclude our survey with a discussion on open theoretical and practical challenges in the field

THE CHANGING STRUCTURE OF RELATIONSHIPS BETWEEN FOREIGN AID AND LOCAL SYSTEMS

This dissertation project examines the extent to which the interaction between the international aid and the public health systems in Thailand generates change in both systems by examining the Global Fund process over the last ten years. This research uses complexity science, network theory, and organizational collaboration literatures, taking Elinor Ostrom’s institutional analysis and development framework as its theoretical foundation. The Global Fund is an action arena that bridges both the local public health action arena and the Thai foreign aid action arena. It creates structures that result in organizational interactions, program design and implementation, and program evaluations that feed back into both the local public health and foreign aid action arenas, resulting in change in both. This project uses document analysis, network analysis and interviews conducted during fieldwork in Thailand to examine how interactions between organizations change the structure of relationships, organizational roles and influence and program outcomes. It finds that the Global Fund process results in network structural and substantive changes, including changes in density, development of sub-network structures and changes in participants and program focus. Through these changes, the process engenders positive adaptation within the public health sector in Thailand, by improving human, organizational and community capacity and by reaching previously underserved populations, and positive adaptation in the foreign aid system in Thailand through the changing the roles of these organizations, adapting from agenda setters to providers of technical assistance. This study makes important contributions to the fields of complexity and systems, organizational collaboration and network theory. It finds that the bridging action arena creates and enhances relationships between organizational members, resulting in adaptation within the arenas it overlaps. The results are changes in the attributes of the community and the rules in which they operate within both systems. It also changes the material conditions of both the systems it overlaps. This study is an exploratory endeavor that seeks to expand the understanding of overlapping systems and contribute to theories surrounding this phenomenon. In the process of this research, theoretical questions emerged about the nature of these overlapping systems, about the participants within them, and about how they develop over time that will inform future research agendas

Using Social Network Analysis to Investigate the Relationship between School-Based Team Communication Networks and Implementation of Positive Behavior Support Systems

The purpose of this study was to examine the relationship between school-based team communication networks and implementation of school-wide reform efforts and initiatives, namely Positive Behavioral Interventions and Supports (PBIS). The study employed social network analysis (SNA) to determine if a relationship was present between the structure and properties of the team communication network and the level of implementation of PBIS, the position and properties of the PBIS leadership team and the level of implementation of PBIS implementation, and the quality of internal process for collaboration of the PBIS leadership team and PBIS implementation. It was predicted that schools in which teachers and staff have opportunities to communicate with their colleagues within and across teams have a network conducive to access of social capital and diffusion of innovation, supporting the school-wide implementation of reform efforts. Team network data were collected from eight elementary schools actively implementing PBIS and were analyzed at the network and ego-level using social network analyses. Network analyses were correlated with reports of PBIS implementation, as measured by the Self-Assessment Survey (SAS). Internal process for collaboration was assessed using the Teacher Collaboration Assessment Survey (TCAS) and correlated with the SAS. Moderate findings were present between network properties indicating the number of nodes, edges, and density of the network and PBIS implementation. A moderate relationship was also found between the degree centrality, betweenness centrality, and eigenvector centrality of the PBIS leadership team and the level of PBIS implementation. Statistically significant and strong correlations were reported for the quality of internal process for collaboration in PBIS leadership teams and PBIS implementation. The study concludes with a discussion of the implications of the findings for policy, professional practice, and future research on implementation of school-wide reform efforts, particularly from a social network and diffusion of innovations perspective