Cluster Detection

Detecting and quantifying clusters in graphs is a highly generalizable problem useful in a myriad of scenarios (e.g. understanding collective behaviour, large-image processing, etc.). Yet there is currently no implementation in GraphX for community detection. That's where I step in.

Gathering Insight

Biomedical research is a rich field of study encompassing many area of multidisciplinary expertise. This tool aims to be a stepping stone towards empowering academics and medical staff for better decision making. Discovering new collaboration and obtaining a clearer understanding of crucial social links between entities are on the table.

Open Source

By making this code publicly available, I hope to help others solving similar problems. Contributing this implementation to the GraphX team by merging with the main branch is also envisioned.


Yearly trends

Personal and community-wide statistics on growth regarding coauthorship of scientific publication.

Graph analytics

How are you positioned inside your community of peers? How could this affect your proficiency?

Hierarchical visualization

Very large graphs are difficult to visualize in any interactive manner. By providing a hierarchical exploration of nodes we allow the user to navigate the complexity.

Community Detection

Observing trends at finer granularity of your population allows crucial insights to be gathered at the right resolution.

World Trends

Number of publication per country

About Me

My name is Sebastien Dery, graduate student in Biomedical Engineering from McGill University, Canada. Please visit my Personal Webpage for more information about me. I also encourage you to reach out on LinkedIn or by email if we share similar interests or simply for a conversation!