About Us

Welcome to NETLAB! Our team conducts research in computational social science and network science. Substantively, our work contributes to environmental social science (e.g. social networks and climate change, the environmental movement, soci-ecological development in the North Atlantic), the sociology of science (e.g. science policy, the evolution of large scale research and development networks, knowledge diffusion, impacts of diversity), and cognitive social science (e.g. integrated models of social and information / belief / knowledge networks, applications of natural language processing and text mining in the social sciences). You can find out about some of our publications here, and our software development projects here.

John McLeveyThe lab is funded in part by grants from the Social Science and Humanities Research Council of Canada (SSHRC) and an Early Researcher Award from the Ontario Ministry of Research and Innovation awarded to the NETLAB Principal Investigator, Dr. John McLevey.

We are currently collaborating with researchers from other institutions in Canada, the US, the UK, Italy, Luxembourg, Germany, and other countries. 

Please contact Dr. John McLevey for more information. 

Training Workshops

We offer methods workshops. You can read about them here. Our plan is to offer workshops annually, usually in early April. This year we are offering workshops on (1) (partially) automating literature reviews for knowledge discovery and synthesis, (2) how to analyze networks with R, and (3) fundamentals of automated content analysis for social scientists

Interested in Joining Us? 

Students in NETLAB are trained in a variety of computational and quantitative methods, from generalized linear models to classical and statistical network analysis, agent-based models, record linkage, and supervised and unsupervised machine learning methods for text analysis. Students are also trained in programming for data analysis, reproducible research techniques, collecting data from the web, from digital archives, using surveys and interviews, and combining quantitative and qualitative methods. Finally, graduate students in NETLAB typically co-author journal articles with Dr. McLevey. 

If you are interested in working in NETLAB or pursing a graduate degree at the University of Waterloo, please send me an email explaining why you are interested in working together and what your educational and / or professional background is. Although there are no open positions right now, there may be in the near future. 

  1. Apr. 8 to 9, 2019How to (partially) automate literature reviews for knowledge discovery and synthesis

    It is more challenging than ever for researchers to be on top of all the latest publications and other developments in their areas of research, regardless of their career stage or the fields they work in. One of the main challenges is the exponential growth of publications. In nearly all fields, there is more work being published every year than any one researcher, or research team, can possibly read and synthesize. This is especially challenging for interdisciplinary researchers, junior researchers, or researchers who are moving into new areas because it can take a fairly long time to get the lay of the land in an unfamiliar literature. This makes research and discovery more difficult than it needs to be for teams and individual researchers, and it holds back the development and communication of collective knowledge. 

    Talking to colleagues and mentors, reading the latest articles in the top-ranked journals, going to conferences, and building diverse research teams are all indispensable strategies for keeping on top of the literature and for discovering and synthesizing new knowledge. However, these strategies (1) are often costly and slow, and (2) are generally biased, though not always in negative ways.

    This workshop will cover another set of tools from network science, text mining, and scientometrics that can help us rapidly get up to speed on the state of knowledge in a field, and to mine existing knowledge to identify promising areas for discovery and further research.

  2. Apr. 10 to 11, 2019How to analyze networks with R

    In this two-day workshop, you will learn a variety of tools for analyzing data on social and information networks using the programming language R. The first day will be an introduction to R and RStudio, followed by a series of classic topics in network analysis, such as centrality analysis and community detection. The second day will cover visualization, handling extremely dense networks, and developing statistical models for network data. 

  3. Apr. 12, 2019Fundamentals of automated content analysis for social scientists

    This one-day workshop offers a practical introduction to fundamentals and recent developments in automated content analysis. The workshop is designed with social scientists in mind, but participants from other fields (including digital humanities) are also welcome. We assume that participants have little to no prior experience with methods for automated content analysis. 

All upcoming events
  1. Mar. 19, 2018Record pair classification with recordlinkage

    Using recordlinkage for classifying candidate record pairs.

  2. Aug. 29, 2017Network analysis with metaknowledge

    Generate and analyze networks with metaknowledge.

  3. Aug. 29, 2017Getting started with metaknowledge

    Learning how to extract and explore records from raw bibliometric data.

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