Dynamic network analytics for recommending scientific collaborators

Collaboration is one of the most important contributors to scientific advancement and a crucial aspect of an academic’s career. However, the explosion in academic publications has, for some time, been making it more challenging to find suitable research partners. Recommendation approaches to help academics find potential collaborators are not new. However, the existing methods operate on static data, which can render many suggestions less useful or out of date. The approach presented in this paper simulates a dynamic network from static data to gain further insights into the changing research interests, activities and co-authorships of scholars in a field–all insights that can improve the quality of the recommendations produced. Following a detailed explanation of the entire framework, from data collection through to recommendation modelling, we provide a case study on the field of information science to demonstrate the reliability of the proposed method, and the results provide empirical insights to support decision-making in related stakeholders—e.g., scientific funding agencies, research institutions and individual researchers in the field

Artificial Intelligence in Business: A Literature Review and Research Agenda

The rise of artificial intelligence (AI) technologies has created promising research opportunities for the information systems (IS) discipline. Through applying latent semantic analysis, we examine the correspondence between key themes in the academic and practitioner discourses on AI. Our findings suggest that business academic research has predominantly focused on designing and applying early AI technologies, while practitioner interest has been more diverse. We examine these differences in the socio-technical continuum context and relate existing literature on AI to core IS research areas. In doing so, we identify existing research gaps and propose future research directions for IS scholars related to AI and organizations, AI and markets, AI and groups, AI and individuals, and AI development

Harnessing Teamwork in Networks: Prediction, Optimization, and Explanation

abstract: Teams are increasingly indispensable to achievements in any organizations. Despite the organizations' substantial dependency on teams, fundamental knowledge about the conduct of team-enabled operations is lacking, especially at the {\it social, cognitive} and {\it information} level in relation to team performance and network dynamics. The goal of this dissertation is to create new instruments to {\it predict}, {\it optimize} and {\it explain} teams' performance in the context of composite networks (i.e., social-cognitive-information networks). Understanding the dynamic mechanisms that drive the success of high-performing teams can provide the key insights into building the best teams and hence lift the productivity and profitability of the organizations. For this purpose, novel predictive models to forecast the long-term performance of teams ({\it point prediction}) as well as the pathway to impact ({\it trajectory prediction}) have been developed. A joint predictive model by exploring the relationship between team level and individual level performances has also been proposed. For an existing team, it is often desirable to optimize its performance through expanding the team by bringing a new team member with certain expertise, or finding a new candidate to replace an existing under-performing member. I have developed graph kernel based performance optimization algorithms by considering both the structural matching and skill matching to solve the above enhancement scenarios. I have also worked towards real time team optimization by leveraging reinforcement learning techniques. With the increased complexity of the machine learning models for predicting and optimizing teams, it is critical to acquire a deeper understanding of model behavior. For this purpose, I have investigated {\em explainable prediction} -- to provide explanation behind a performance prediction and {\em explainable optimization} -- to give reasons why the model recommendations are good candidates for certain enhancement scenarios.Dissertation/ThesisDoctoral Dissertation Computer Science 201

Air Force Institute of Technology Research Report 2020

This Research Report presents the FY20 research statistics and contributions of the Graduate School of Engineering and Management (EN) at AFIT. AFIT research interests and faculty expertise cover a broad spectrum of technical areas related to USAF needs, as reflected by the range of topics addressed in the faculty and student publications listed in this report. In most cases, the research work reported herein is directly sponsored by one or more USAF or DOD agencies. AFIT welcomes the opportunity to conduct research on additional topics of interest to the USAF, DOD, and other federal organizations when adequate manpower and financial resources are available and/or provided by a sponsor. In addition, AFIT provides research collaboration and technology transfer benefits to the public through Cooperative Research and Development Agreements (CRADAs). Interested individuals may discuss ideas for new research collaborations, potential CRADAs, or research proposals with individual faculty using the contact information in this document

A Comprehensive Survey on Deep Graph Representation Learning

Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been widely studied in a range of fields, including machine learning and data mining. Classic graph embedding methods follow the basic idea that the embedding vectors of interconnected nodes in the graph can still maintain a relatively close distance, thereby preserving the structural information between the nodes in the graph. However, this is sub-optimal due to: (i) traditional methods have limited model capacity which limits the learning performance; (ii) existing techniques typically rely on unsupervised learning strategies and fail to couple with the latest learning paradigms; (iii) representation learning and downstream tasks are dependent on each other which should be jointly enhanced. With the remarkable success of deep learning, deep graph representation learning has shown great potential and advantages over shallow (traditional) methods, there exist a large number of deep graph representation learning techniques have been proposed in the past decade, especially graph neural networks. In this survey, we conduct a comprehensive survey on current deep graph representation learning algorithms by proposing a new taxonomy of existing state-of-the-art literature. Specifically, we systematically summarize the essential components of graph representation learning and categorize existing approaches by the ways of graph neural network architectures and the most recent advanced learning paradigms. Moreover, this survey also provides the practical and promising applications of deep graph representation learning. Last but not least, we state new perspectives and suggest challenging directions which deserve further investigations in the future

Mentorship: A Powerful Tool for IPG Success

Because Canada espouses principles of diversity and multiculturalism, many international pharmacy graduates (IPGs) immigrate to Canada expecting to find employment using skills for which they trained in their home country. Upon arrival, they often face challenges in credential recognition and licensure. Barriers include systemic discrimination, socio-psychological isolation, the precipitous decline in social status, and financial challenges of navigating the steps that bridge the training received in their home countries to the scopes of practice in Canada. The problem of practice (PoP) explored in this organizational improvement plan (OIP) focuses on the lack of opportunity that IPGs have to access clinical workplace settings prior to being assessed for entry to practice competencies. Health Alliance is an organization that works in the regulatory space for internationally educated healthcare professionals, and that provides a service that facilitates the IPG path to licensure in Canada. This OIP proposes housing a mentorship program at Health Alliance, to specifically address the experiential learning, and knowledge and skill gaps that have been identified as barriers to success for international pharmacy graduates pursuing licensure, and ultimately, gainful employment as pharmacists in Canada. This OIP examines the PoP through the lenses of sense of community theory and critical race theory to explore how the lived experiences of diverse internationally educated skilled immigrants are impacted by the process of seeking credential recognition and licensure. Change at the leadership, cultural and operational levels will be facilitated through Kotter’s eight stage change model and will be evaluated using an empowerment evaluation approach