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 However, the existing methods operate on static data, which can render many suggestions less useful or out of date.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

Expert recommendation based on social drivers, social network analysis, and semantic data representation

ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration

To whom and why should I connect? Co-author Recommendation based on Powerful and Similar Peers

Sie, R. L. L., Drachsler, H., Bitter-Rijpkema, M., & Sloep, P. B. (2012). To whom and why should I connect? Co-author Recommendation based on Powerful and Similar Peers. International Journal of Technology Enhanced Learning (IJTEL), 4(1), 121-137. doi:10.1504/IJTEL.2012.048314The present article offers preliminary outcomes of a user study that investigated the acceptance of a recommender system that suggests future co- authors for scientific article writing. The recommendation approach is twofold: network information (betweenness centrality) and author (keyword) similarity are used to compute the utility of peers in a network of co-authors. Two sets of recommendations were provided to the participants: Set one focused on all candidate authors, including co-authors of a target user to strengthen current bonds and strive for acceptance of a certain research topic. Set two focused on solely new co-authors of a target user to foster creativity, excluding current co- authors. A small-scale evaluation suggests that the utility-based recommendation approach is promising, but to maximize outcome, we need to 1) compensate for researchers’ interests that change over time, and 2) account for multi-person co-authored papers

A Survey of Scholarly Data: From Big Data Perspective

Recently, there has been a shifting focus of organizations and governments towards digitization of academic and technical documents, adding a new facet to the concept of digital libraries. The volume, variety and velocity of this generated data, satisfies the big data definition, as a result of which, this scholarly reserve is popularly referred to as big scholarly data. In order to facilitate data analytics for big scholarly data, architectures and services for the same need to be developed. The evolving nature of research problems has made them essentially interdisciplinary. As a result, there is a growing demand for scholarly applications like collaborator discovery, expert finding and research recommendation systems, in addition to several others. This research paper investigates the current trends and identifies the existing challenges in development of a big scholarly data platform, with specific focus on directions for future research and maps them to the different phases of the big data lifecycle

You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems

Visual query systems (VQSs) empower users to interactively search for line charts with desired visual patterns, typically specified using intuitive sketch-based interfaces. Despite decades of past work on VQSs, these efforts have not translated to adoption in practice, possibly because VQSs are largely evaluated in unrealistic lab-based settings. To remedy this gap in adoption, we collaborated with experts from three diverse domains---astronomy, genetics, and material science---via a year-long user-centered design process to develop a VQS that supports their workflow and analytical needs, and evaluate how VQSs can be used in practice. Our study results reveal that ad-hoc sketch-only querying is not as commonly used as prior work suggests, since analysts are often unable to precisely express their patterns of interest. In addition, we characterize three essential sensemaking processes supported by our enhanced VQS. We discover that participants employ all three processes, but in different proportions, depending on the analytical needs in each domain. Our findings suggest that all three sensemaking processes must be integrated in order to make future VQSs useful for a wide range of analytical inquiries.Comment: Accepted for presentation at IEEE VAST 2019, to be held October 20-25 in Vancouver, Canada. Paper will also be published in a special issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) IEEE VIS (InfoVis/VAST/SciVis) 2019 ACM 2012 CCS - Human-centered computing, Visualization, Visualization design and evaluation method

Identifying effective criteria for author matching in bioinformatics

With the increasing development of information and scientific databases, scientific collaboration has expanded in health sciences. This study aims to prioritize the criteria that affect finding potential author matches in bioinformatics using fuzzy Multiple Criteria Decision Making (MCDM) methods such as Analytical Hierarchy Process (AHP), Fuzzy Delphi Method (FDM), and Triangular Fuzzy Numbers (TFN). To answer the research questions, a mix of documentary analysis and fuzzy methods is utilized. The documentary analysis stage involves collecting relevant documents and resources using the purposive sampling approach and ranking the effective criteria. The subsequent step involves experts determining the priorities of the effective criteria using pairwise comparisons and the Delphi questionnaire. The final weights are obtained based on the research purpose. The study shows that 79 criteria related to the research purpose can be grouped into three general categories: behavioral, topological, and content-based criteria. The most effective criteria in finding and recommending a potential author match are “journal titles”, “citations”, “paper titles”, “affiliations”, “keywords”, and “abstracts”. Among these criteria, citation and paper titles have a higher priority compared to others. The results indicate that contentbased criteria have the most significant impact on finding potential author matches in static scholar networks and networks with text information. Furthermore, among the content-based criteria, the number of publications in common specialized journals and the number of common citations are the most sought-after criteria for finding a potential author match with the highest similarity

Community Design of a Knowledge Graph to Support Interdisciplinary PhD Students

This is the submitted version of the paper, pre-revision.How do PhD students discover the resources and relationships conducive to satisfaction and success in their degree programs? This study proposes a community-grounded, extensible knowledge graph to make explicit and tacit information intuitively discoverable, by capturing and visualizing relationships between people based on their activities and relations to information resources in a particular domain. Students in an interdisciplinary PhD program were engaged through three workshops to provide insights into the dynamics of interactions with others and relevant data categories to be included in the graph data model. Based on these insights we propose a model, serving as a testbed for exploring multiplex graph visualizations and a potential basis of the information system to facilitate information discovery and decision-making. We discovered that some of the tacit knowledge can be explicitly encoded, while the rest of it must stay within the community. The graph-based visualization of the social and knowledge networks can serve as a pointer toward the people having the relevant information, one can reach out to, online or in person