Analyzing, Mining, and Predicting Networked Behaviors

Network structure exists in various types of data in the real world, such as online and offline social networks, traffic networks, computer networks, brain networks, and countless other cases where there are relationships between different entities in the data. What are the roles of network structures in these data? First, the network captures inherent characteristics of the data themselves. This is clear from the definition of the network, which represents the relationship between entities: e.g., the social links among people in a social network describe how they interact with each other; a road network summarizes how the roads are laid out geographically; a brain network obtained from fMRI images represents pairs of brain regions that are active at the same time; a computer network constrains the paths via which internet packages and thus information or viruses can spread. Second, the network structures affect the evolution of the data over time. For example, new friendship links in an online social network are frequently created between friends of friends. Similarly, the current road network structure is without a doubt taken into consideration when roads are added or temporarily closed. As we grow, our brains also grow, including the additions of useful links or the clean up of unnecessary links between brain regions. Third, the network structures act as guidance for many different processes happening in the data. For instance, the links between users on social network dictate how gossips can spread; the roads influence how traffic flows in a city; the links between brain regions affects the way we think and how effectively we do things; the connections between computers route the transfer of any information on the internet.In this thesis, I studied the network effect in various networked behaviors, including analyzing such effect, finding its patterns, and predicting future networked behaviors. First, I gained insights into the data by analyzing the accompanied network structures as well as its evolution. Second, I proposed algorithms for mining different network patterns that help summarize the effect of the network structures on different networked behaviors. Finally, I proposed models to predict the evolution of networked behaviors over time. Toward these tasks, I explored a wide variety of network data, including protein-protein interaction networks, online social networks, collaboration networks, chemical compounds, and traffic networks. Overall, I tackled these network data in different aspects and developed a number of methods for effectively mining and forecasting networked behaviors in data

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

Office of Research and Graduate Studies -- Annual Report 2003-2004

Contents Science and Engineering page Recombinant Bandage 02 Advanced Computing, Everyday Life 04 Software Engineering 05 Silicon Nanotubes. 06 Nutritional Genomics and Nanomaterials. 08 Genetics of Virulence 09 Rapid PCR Device. 10 Selenium, Cancer and Aging 12 Education Math in the Middle Institute. 14 Project Fulcrum 16 School Readiness for Parents 17 Behavioral Science Assessing Threatening Behavior 18 Family Dynamics of Infertility 19 Arts & Humanities Encyclopedia of the Great Plains 20 Global Politics 22 Sculpture Conservation. 22 Commission with Philip Glass 23 Rising Stars page Luminescence. 24 Giant Thunderstorms 24 Debugging Software 25 Technology Development Robotic Traffic Barrels 26 Buffalograss for Turf 26 Textiles from Cornhusks 27 Graduate Studies Undergraduate Research 28 Professional Development 29 Interactive Economics Education 29 Extending Our Reach The Nebraska Lectures 30 Research Fair 2004 30 Water Law, Science and Policy 31 Financials: FY 2003-2004 3

A Social Networks Approach to Interaction Patterns of BTS compared to Justin Bieber on Twitter

A Korean-pop boy band, BTS, has broken the cultural barrier to make changes in the nature of the global pop industry. This study examined the unique approach BTS has taken through social media to building its fan base and interacting with its fans. Twitter datasets were analyzed to explore the nature of BTS’s interaction with its fans on social media, as compared to another pop star, Justin Bieber. Findings and implications are discussed

The Global People competency framework: competencies for effective intercultural interaction

This Competency Framework explains the competencies that are needed for effective intercultural interaction. In contrast to the Life Cycle Model for Intercultural Partnerships (see the Global People Toolbook) which presents the competencies by stage (i.e. key competencies are identified for each stage of a project life cycle), the Competency Framework presents them by clusters. Intercultural competencies can be grouped into four interrelated clusters, according to the aspect of competence they affect or relate to: - Knowledge and ideas - Communication - Relationships - Personal qualities and dispositions We overview these four clusters in Section 2. In Sections 3 – 6, for each competency cluster, we list the key component competencies, along with descriptive explanations of each of them. We also provide case study examples from the eChina-UK Programme to illustrate one or more of the following: - How the competency manifests itself; - Why the competency is important or is needed; - How the competency can be displayed in behaviour; - What problems may occur when the competency is not present. The Competency Framework is thus useful for those who wish to gain a systematic, in-depth understanding of intercultural effectiveness and the competencies need to achieve it

Far better than nothing at all: Towards a contingency-based evaluation of management consulting services

The evaluation of consulting services is a widely, but controversially discussed issue. While there is obviously an increasing need for appropriate evaluation, there are critics questioning the applicability of evaluation methods as well as the motivation to assess. We provide a framework that can be used to improve project evaluations and structures future academic and practical discussions as it integrates relevant contingency factors (project type, consultant-client-relationship, characteristics of the evaluator) that affect opportunities and abilities to evaluate project input, throughput, and output. We summarize the prospects and challenges for the research agenda and suggest some management implications. --Management consulting,evaluation,performance,contingency factors