Today’s social and internet networks contain millions or even billions of nodes, and copious amounts of side information (context) such as text, attribute, temporal, image and video data. A thorough analysis of a social network should consider both the graph and the associated side information, yet we also expect the algorithm to execute in a reasonable amount of time on even the largest networks. Towards the goal of rich analysis on societal-scale networks, this thesis provides (1) modeling and algorithmic techniques for incorporating network context into existing network analysis algorithms based on statistical models, and (2) strategies for network data representation, model design, algorithm design and distributed multi-machine programming that, together, ensure scalability to very large networks. The methods presented herein combine the flexibility of statistical models with key ideas and empirical observations from the data mining and social networks communities, and are supported by software libraries for cluster computing based on original distributed systems research. These efforts culminate in a novel mixed-membership triangle motif model that easily scales to large networks with over 100 million nodes on just a few cluster machines, and can be readily extended to accommodate network context using the other techniques presented in this thesis.

M. Tech. Business Information Systems / The growing trends and the rapid developments of technological innovation have led to a new way of communication. These developments have seen individuals and organizations spending a lot of money on technological devices, software and applications much higher than ever before. On the other hand, institutions of learning are also advancing with technological innovations by shifting away from the face-to-face teaching and communicating with learners, to the use of Learning Management Systems (LMS). Amidst these challenges, these institutions of learning could leverage on freely available social networks for communication and for teaching and learning. However, these transitions have been impeded by many factors that need to be explored in order to adopt social networks for teaching and learning. The foremost objective of this study was to develop a framework for the adoption of social networks for teaching and learning at high schools.

M. Tech. Business Information Systems / Focuses on tackling the lack of access to learning content and social resources in the higher learning environment of South Africa (SA). This research is important because South African institutions of higher learning are English language environments dominated by underprepared learners and overpopulated classrooms. Moreover, the country has lately seen increased numbers of learners entering higher learning institutions demonstrating a fair rate of acceptance of social media sites (SMS).

Thesis (MTech. degree in Business Information Systems.)--Tshwane University of Technology, 2010. / This research addresses how mobile learning by means of a virtual community of practice can facilitate interaction and knowledge sharing amongst contact university learners as they traverse varied learning environments. Using Activity theory and the theory of social presence, the research aimed to provide an informed understanding of virtual communities of practice and how they manifest in a mobile learning environment. In this dissertation, virtual communities of practice are argued to effectively facilitate personalized learning support in an environment where learning is not confined to particular places. Importantly, virtual communities of practice are fundamentally modeled by awareness of context and social presence. Informed by empirical evidence gathered through a Contextual Inquiry method, a field research framework that depends on interaction with users in the context of their work, this study shows how mobile learners in a typical South African university could be afforded personalized academic support as they traverse varied learning environments.

The business environment has been changing as it becomes easier to interact acrossboundaries with globalisation. One of the key elements of globalisation is the culturaldiversity resulting from the cross-cultural and ethnic interactions between individuals.This constant growing globalisation challenges small and medium-sized enterprises(SMEs) to interact with different cultural backgrounds in their foreign markets and withtheir local staff. This cultural diversity can bring both positive and negative outcomes toSMEs depending on how they approach these challenges.The purpose of our research is to discover the contribution of cultural diversity in theinternationalisation process of a Swedish SME, named CodeMill and to understand howthis contribution is ensured by this particular SME. Our research focuses on two specificcriteria of cultural diversity, which are expressed as (1) the individual's internationalexperience as an employee and (2) the SME's social network abroad. Our studyprovides them with practical contributions presented in a final framework, whichexplains how to take advantage of cultural diversity to enhance the positive outcomes ofit and strengthen their internationalisation process.A qualitative case study was conducted with CodeMill, a locally based InformationTechnology (IT). They fulfilled the principal requirements in terms of employees andyearly turnover in order to be categorised as an SME. Conducting seven semi -structured interviews in total, with people from different hierarchical levels and spheresenabled us to gain insights on how matters relating to cultural diversity are handled inCodeMill. Secondary Data served to confirm information we received from theinterviews and functioned as an additional source of information.The study proposes a framework that has been revised from the analysis of ourempirical findings. This framework is positioned within the field of Cultural DiversityManagement in an internationalisation context. The gathered findings implicate that thelevel of informational diversity, which needs to be used with a high synergy level,determines the importance of the contribution of cultural diversity. This can be ensuredvia three key elements: leadership, research & measurement, and follow-up. Thecompany’s Entrepreneurial Orientation (EO), especially included the three investigateddimensions of innovativeness, proactiveness and risk-taking proved to be applied inCodeMill. They were considered to have a positive influence on the internationalisationprocess of the company. CodeMill enhances its internationalisation process thanks to sixcompetitive advantages ensuing from its level of cultural diversity. However its socialnetwork hinders the opportunities to enter new markets. We found proof that CodeMillbenefits strongly from connections at an organisational level (e.g. partner companies,international customers); whilst an individual’s international experience, gathered fromliving abroad or just having personal international contacts, did not significantlycontribute to the firm’s internationalisation.

Despite decades of effort in academia and industry, it is not yet possible to build machines that can replicate many seemingly-basic human perceptual abilities. This work focuses on the problem of face identification that most of us effortlessly solve daily. Substantial progress has been made towards the goal of automatically identifying faces under tightly controlled conditions; however, in the domain of unconstrained face images, many challenges remain. We observe that the recent combination of widespread digital photography, inexpensive digital storage and bandwidth, and online social networks has led to the sudden creation of repositories of billions of shared photographs and opened up an important new domain for unconstrained face identification research. Drawing upon the newly-popular phenomenon of “tagging,” we construct some of the first face identification datasets that are intended to model the digital social spheres of online social network members, and we examine various qualitative and quantitative properties of these image sets. The identification datasets we present here include up to 100 individuals, making them comparable to the average size of members’ networks of “friends” on a popular online social network, and each individual is represented by up to 100 face samples that feature significant real-world variation in appearance, expression, and pose. We demonstrate that biologically-inspired visual representations can achieve state-of-the-art face identification performance on our novel frontal and multi-pose face datasets. We also show that the addition of a tree-structured classifier and training set augmentation can enhance accuracy in the multi-pose setting. Finally, we illustrate that the machine-readable “social context” in which shared photos are often embedded can be applied to further boost face identification accuracy. Taken together, our results suggest that accurate automated face identification in vast online shared photo collections is now feasible. / Engineering and Applied Sciences

As the Internet is becoming information-centric, network services increasingly demand scalable and efficient communication of information between a multitude of information producers and large groups of interested information consumers. Such information-centric services are growing rapidly in use and deployment. Examples of deployed services that are information-centric include: IPTV, MMORPG, VoD, video conferencing, file sharing, software updates, RSS dissemination, online markets, and grid computing. To effectively support future information-centric services, the network infrastructure for multi-point communication has to address a number of significant challenges: (i) how to understand massive information-centric groups in a scalable manner, (ii) how to analyze and predict the evolution of those groups in an accurate and efficient way, and (iii) how to disseminate content from information producers to a vast number of groups with potentially long-lived membership and highly diverse, dynamic group activity levels? This dissertation proposes novel architecture and protocols that effectively address the above challenges in supporting multi-point communication for future information-centric network services. In doing so, we make the following three major contributions: (1) We develop a novel technique called Proximity Embedding (PE) that can approximate a family of path-ensembled based proximity measures for information-centric groups. We develop Clustered Spectral Graph Embedding (SCGE) that captures the essential structure of large graphs in a highly efficient and scalable manner. Our techniques help to explain the proximity (closeness) of users in information-centric groups, and can be applied to a variety of analysis tasks of complex network structures. (2) Based on SCGE, we develop new supervision based link prediction techniques called Clustered Spectral Learning and Clustered Polynomial Learning that enable us to predict the evolution of massive and complex network structures in an accurate and efficient way. By exploiting supervised information from past snapshots of network structures, our methods yield up to 20% improvement in link prediction accuracy when compared to existing state-of-the-art methods. (3) Finally, we develop a novel multicast infrastructure called Multicast with Adaptive Dual-state (MAD). MAD supports large number of group and group membership, and efficient content dissemination in a presence of dynamic group activity. We demonstrate the effectiveness of our approach in extensive simulation, analysis, and emulation through the real system implementation. / text

Social media have been growing rapidly in recent years thanks to the innovations of social networking sites (SNS) such as Facebook and Twitter, both of which originated in the United States. Currently, SNS and other social media have become global phenomena. This report aims to study the features of SNS that prosper in the U.S., Japan, and China. Through a comparative analysis of the similarities and differences among the top SNS players in each of the three countries, it is possible to identify unique characteristics of each nation’s social networking landscape. The SNS market in the U.S. is relatively mature, while the social networking population is growing in Japan and China. However, contrary to the expectations of some, the Japanese and Chinese social networking landscapes appear to be quite different from one another with regard to SNS usage, despite Japan and China’s similar cultural backgrounds and geographical proximity. / text

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Recommender Systems are used to select online information relevant to a given user. Traditional (memory based) recommenders explore the user-item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly. With the growing popularity of social networks, recommender systems can benefit from combining history of user preferences with information from the social/trust network of users. This thesis explores two techniques of combining user-item rating history with trust network information to make better user-item rating predictions. The first approach (SCOAL [5]) simultaneously co-clusters and learns separate models for each co-cluster. The co-clustering is based on the user features as well as the rating history. This captures the intuition that certain groups of users have similar preferences for certain groups of items. The grouping of certain users is affected by the similarity in the rating behavior and the trust network. The second graph-based label propagation approach (MAD [27]) works in a transductive setting and propagates ratings of user-item pairs directly on the user social graph. We evaluate both approaches on two large public data-sets from Epinions.com and Flixster.com. The thesis is amongst the first to explore the role of distrust in rating prediction. Since distrust is not as transitive as trust i.e. an enemy's enemy need not be an enemy or a friend, distrust can't directly replace trust in trust propagation approaches. By using a low dimensional representation of the original trust network in SCOAL, we use distrust as it is and don't propagate it. Using SCOAL, we can pin-point the groups of users and the groups of items that have the same preference model. Both SCOAL and MAD are able to seamlessly integrate side information such as item-subject and item-author information into the trust based rating prediction model. / text