Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments

Collaboration in scientific digital ecosystems: A socio-technical network analysis

This dissertation seeks to understand the formation, operation, organizational (collaboration) and the effect of scientific digital ecosystems that connect several online community networks in a single platform. The formation, mechanism and processes of online networks that influence members output is limited and contradictory. The dissertation is comprised of three papers that are guided by the following research questions: How does online community member’s productivity (or success) depend upon their ‘position’ in the digital networks? What are the network formation mechanism, structures and characteristics of an online community? How do scientific innovations traverse (diffuse) amongst users in online communities? A combination of exploratory, inductive and deductive research designs is applied sequentially but in a non-linear manner to address research question. The dissertation contributes to the literature on scientific collaboration, digital communities of creation, social network modelling and diffusion of innovation. The first paper applies network theory and spatial probit autocorrelative modelling technique to evaluate how member developer’s positioning in digital community correlate with his/her productivity. The second paper looks at the dynamics of developer’s participation in online developers’ network for a period spanning 7-years using exponential random graph models (ERGM). This paper applies theory of network (network science) to model network formation patterns in developer community. The third paper, like the first, applies network theory and to understand user network characteristics and communication channels which influence diffusion of scientific innovations. Bass and spatial probit autocorrelative models are applied for this analysis. Data from this study was mined from developers, authors and user communities of nanoHUB.org cyberinfrastructure platform. NanoHUB.org is a science and engineering online ecosystem comprising self-organized researchers, educators, and professional communities in eight member institutions that collaborate, share resources and solve nanotechnology related problems including development and usage of tools (scientific innovation). Data from collaboration and information sharing activities was used to create the developers, authors and user networks that were used for analysis. Results of the first paper show that the spatial autocorrelation parameter of the spatial probit model is negative and statistically different from zero. The negative spatial spillover effect in the developer network imply that developers that are embedded in the network have a lower probability of getting more output. The structural network characteristics of eigen vector centrality had statistically significant effects on probability of being more productive. Developers who are also authors were found to be more productive than those in one network. The implications of these findings is that developers will benefit from being in multiple network spaces and by associating with more accomplished developers. The autocorrelative and interaction models also reveal various new modelling approach of accounting for network autocorrelation effects to online member. Results of the second paper show that developers form in a manner that follow a pure uniform random distribution. Results also show that developer’s collaborative mechanisms are characterized by low tendencies to reciprocate and form homophiles (tendency of developers to associate with similar peers) but high tendency to form clusters. The implications of network formation mechanism and processes are that developers are forming in a purely random and self-organized manner and minimum efforts should be applied in trying to organize and influence the community organization. The results also reveal that a simple link to link ERGM and stochastic dominance criteria can be combined to characterize the network formation characteristics just like the ERG(p*) model but have an advantage of overcoming degeneracy challenges associated with ERG(p*) models. Results of the third paper show that bass model is a good predictor for diffusion of scientific innovations (tools) in online community setting. Results also show different innovations have varying levels and rates of adoption and these were influenced by both external and internal factors. Results of the micro-based model found degrees and betweeness centrality as some of the internal variables that have positive influence on the adoption of innovation while centrality measures of power or leadership were found to have negative influence of adoption process. The relative time taken to run a simulation (measured as job usage time) was also found to be negatively influencing diffusion. The implication of the study results is that bass model is a good fit for evaluating and forecasting adoption of innovation in online communities. Moreover, network structural characteristics are responsible for adoption of innovation adoption and policy making should consider tool adoption enhancing ones. Additionally, researchers could further explore the network structural characteristics that are driving diffusion of innovation

Information Filtering on Coupled Social Networks

In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm based on the coupled social networks, which considers the effects of both social influence and personalized preference. Experimental results on two real datasets, \emph{Epinions} and \emph{Friendfeed}, show that hybrid pattern can not only provide more accurate recommendations, but also can enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding structure and function of coupled social networks

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks

Identifying the most influential spreaders that maximize information flow is a central question in network theory. Recently, a scalable method called "Collective Influence (CI)" has been put forward through collective influence maximization. In contrast to heuristic methods evaluating nodes' significance separately, CI method inspects the collective influence of multiple spreaders. Despite that CI applies to the influence maximization problem in percolation model, it is still important to examine its efficacy in realistic information spreading. Here, we examine real-world information flow in various social and scientific platforms including American Physical Society, Facebook, Twitter and LiveJournal. Since empirical data cannot be directly mapped to ideal multi-source spreading, we leverage the behavioral patterns of users extracted from data to construct "virtual" information spreading processes. Our results demonstrate that the set of spreaders selected by CI can induce larger scale of information propagation. Moreover, local measures as the number of connections or citations are not necessarily the deterministic factors of nodes' importance in realistic information spreading. This result has significance for rankings scientists in scientific networks like the APS, where the commonly used number of citations can be a poor indicator of the collective influence of authors in the community.Comment: 11 pages, 4 figure

Social Justice Documentary: Designing for Impact

Explores current methodologies for assessing social issue documentary films by combining strategic design and evaluation of multiplatform outreach and impact, including documentaries' role in network- and field-building. Includes six case studies

Shaping the criminal justice system: the role of those supported by Criminal Justice Service

In Scotland, the development and delivery ofpersonalised social work services has been part of a wider public service reform agenda, building on Changing lives: report of the 21st century review of social work (Scottish Executive, 2006). This agenda has focused on harnessing the strengths, predilections, networks and capacities of those supported by services, to inform the design and delivery of services. To date, the place of criminal justice in this reform agenda has received comparatively limited attention (Weaver, 2011). This Insight focuses on the issue of involving those who have offended in shaping the criminal justice system, exploring the different models of involvement, the effectiveness of different approaches and the implications for Criminal Justice Social Work services