Network dynamics of mobile social networks

With the rapid development of smartphones and mobile Internet technology, we witness an overwhelming growth of mobile social networks (MSN), which is a type of social network, forming virtual communities among people with similar interests or commonalities. In MSNs, users play a crucial role for their development, deployment and success. Understanding the MSN user behavior therefore attracts interests of different entities - ISPs, service providers, and researchers. However, it is hard to gather a comprehensive real data set, little is known and even less has been published about MSN user activities. In this paper, we focus on analyzing MSN user behavior from the perspective of ISP network, which is seldom reported in literature. Based on the real data set collected from the mobile network gateway of a major mobile carrier who has more than five million subscribers, we present an in-depth user behavior analysis of four popular social networks. We study the MSN user behavior from six aspects: user requests, active online time, sessions, inter-session, the number of requests in a session, and inter-request. We found that power law and lognormal are two popular features of the studied objects, and exposed some interesting findings as well. We hope our work could be helpful for ISPs, MSN content providers, and researchers. © 2014 IEEE.

Mining mechanism of top-k influential nodes based on voting algorithm in mobile social networks

In recent years, evaluating the influence of nodes and finding top-k influential nodes in social networks, has drawn a wide attention and has become a hot-pot research issue. Considering the characteristics of social networks, we present a novel mechanism to mine the top-k influential nodes in mobile social networks. The proposed mechanism is based on the behaviors analysis of SMS/MMS (simple messaging service / multimedia messaging service) communication between mobile users. We introduce the complex network theory to build a social relation graph, which is used to reveal the relationship among people's social contacts and messages sending. Moreover, intimacy degree is also introduced to characterize social frequency among nodes. Election mechanism is hired to find the most influential node, and then a heap sorting algorithm is used to sort the voting results to find the k most influential nodes. The experimental results show that the mechanism can finds out the most influential top-k nodes efficiently and effectively. © 2013 IEEE.

Improving data forwarding in mobile social networks with infrastructure support: a space-crossing community approach

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of delivery ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms. © 2014 IEEE.

Space-crossing : community-based data forwarding in mobile social networks under the hybrid communication architecture

In this paper, we study two tightly coupled issues, space-crossing community detection and its influence on data forwarding in mobile social networks (MSNs). We propose a communication framework containing the hybrid underlying network with access point (AP) support for data forwarding and the base stations for managing most of control traffic. The concept of physical proximity community can be extended to be one across the geographical space, because APs can facilitate the communication among long-distance nodes. Space-crossing communities are obtained by merging some pairs of physical proximity communities. Based on the space-crossing community, we define two cases of node local activity and use them as the input of inner product similarity measurement. We design a novel data forwarding algorithm Social Attraction and Infrastructure Support (SAIS), which applies similarity attraction to route to neighbor more similar to destination, and infrastructure support phase to route the message to other APs within common connected components. We evaluate our SAIS algorithm on real-life datasets from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs. Based on this new type of community, SAIS achieves a better performance than existing popular social community-based data forwarding algorithms in practice, including Simbet, Bubble Rap and Nguyen's Routing algorithms.

Developing and asynchronous learning network

Deakin University, Australia, has committed resources over a number of years to developing the use of information and communication technologies in all aspects of teaching and learning. This paper focuses on the development over a four year period of an Asynchronous Learning Network (ALN) for distance education students studying undergraduate introductory macroeconomics. The research is based on quantitative and qualitative data gained from student evaluations, academic staff interviews, participation levels and an analysis of the online communication. Key findings from the research relate to the quality of the learning environment, the level of communication, and the role of academic staff in the learning experience. Strategies discussed for the successful use of an ALN include the nurturing of a collaborative learning environment, the adaptation of curriculum and pedagogy, the role of assessment, and the role of academic staff training and development.

Assessing sustainability : a technical fix or a means of social learning?

The contextual nature of sustainability makes it extremely difficult to measure. Across the world each region has a specific environment and history that has shaped values and perceptions of the local community. In response, a wide range of tools has been developed that employ differing techniques to measure sustainability. These have a range of applications from global to site-specific scales. However, it is yet to be resolved whether assessments made using these tools reflect a technical focus with no close links to the knowledge and perceptions of stakeholders, community and management decisions, or whether the assessments reflect the knowledge and perceptions of local stakeholders and the community. In the southwest region of Victoria, Australia, a sustainability index called AIRS (An Index of Regional Sustainability) has recently been developed. This tool is based on indicators selected by stakeholders and considers relationships between the indicators. The aim of this paper is to report an ex-post evaluation of the AIRS sustainability assessment conducted at a subcatchment scale. The evaluation assesses AIRS's ability to assess regional sustainability and compares and contrasts the subcatchment assessments with the knowledge and perceptions of stakeholders and the community. A participatory approach that acknowledges key stakeholders was used for the evaluation process. Representatives from four stakeholder groups were interviewed to elicit their views of the AIRS assessment, its relevance, quality, applicability and priorities for future development. Strengths and weaknesses of AIRS are revealed and its contribution to social learning is highlighted.

Computational studies to understand the role of social learning in team familiarity and its effects on team performance

This paper concerns social learning modes and their effects on team performance. Social learning, such as by observing others' actions and their outcomes, allows members of a team to learn what other members know. Knowing what other members know can reduce task communication and co-ordination overhead, which helps the team to perform faster since members can devote their attention to their tasks. This paper describes agent-based simulation studies using a computational model that implements different social learning modes as parameters that can be controlled in the simulations. The results show that social learning from both direct and indirect observations positively contributes to learning about what others know, but the value of social learning is sensitive to prior familiarity such that minimum thresholds of team familiarity are needed to realise the benefits of social learning. This threshold increases with task complexity. These findings clarify the level of influence that sociality has on social learning and sets up a formal framework by which to conduct studies on how social context influences learning and group performance.

The challenges and risks of measuring performance in a social learning system

This submission draws attention to the challenges of modelling and measuring teacher performance and the risks associated with the measurement of student and teacher performance. The first premise of this submission is that all such performances are strongly contextual, and that therefore valid modelling and measurement must take the effective dimensions of this context into account. The second premise of this submission is that this performance-context nexus operates as a system and that one of the properties of such systems is continuous feedback looping. The submission draws attention as well to an important entailment of this argument, which is that invalid modelling and measurement may lead to a distortion of the productive functioning of learning systems. This submission accordingly urges the Commission to commit to developing an econometric model which reckons with the contextual determinants of student and teacher performance, and the pursuit of system-wide productivity increases on the basis of the school learning system as the basic unit of analysis. The Commission is also urged to investigate the suggestion that performance measurement regimes which take the basic units of measurement as individual student and teacher performance rather than the school pose a risk to the productivity of schools as learning systems and innovation in the Australian economy as a whole in the longer term.

Knowledge mobilization on decision making for school improvement : Canadian principals learning network.

Principals duties have expanded beyond instructional leadership. Roles now include being curriculum leader, supervisor, manager, head of finance, administration, compliance, and legal matters, and so on. These additional responsibilities impact their decision-making in relation to teaching, learning and school improvement in general. How, and on what basis, they make these decisions is crucial both to their development as instructional leaders and to educational reform processes. To contribute to knowledge on principals’ decision making skills, we have created a strategic knowledge mobilization initiative called 'Canadian Principals Learning Network (CPLN)'. Through a variety of face-to-face and online activities, it collaboratively links together an international group of practicing principals and university-based researchers with related expertise. This paper describes the initiative and outcomes.

Learning in social action : the informal and social learning dimensions of circumstantial and lifelong activists

This paper explores the informal and social learning dimensions of activists as they learn skills and knowledge through participating in social action. In doing this I draw on Lave and Wenger's epistemology of situated learning and Bourdieu's theory of "habitus". I argue activists learn an array of community development skills in the social environment of activism. I claim activists' learning is cognitive, embodied and situated in practice. This paper is based on empirical research in Australia, where in-depth interviews were conducted with activists to uncover their important pedagogy. It explores the learning dimensions of two groups of activists. "Lifelong activists" who have generally been involved in student politics and have participated in activism over many years, and "circumstantial activists" who become involved in protest due to a series of life circumstances. This paper claims that while both groups' learning is social and informal, lifelong activists tend to develop their skills incrementally by being involved in the fertile site of student politics. On the other hand, circumstantial activists, not having had the benefit of early immersion in a community of practice, are rapid learners. They are frequently taken out of their comfort zone as activists and need to acquire new knowledge and skills urgently in order to practise effectively. Some circumstantial activists remain on the periphery of activism and never fully immerse themselves in the practices of activism. I argue there is much to be gained from understanding learning in social action, an epistemology of adult learning which deserves greater prominence in current adult education discourse.