Welcome to the DC/NoVA Papers We Love meetup!
Papers We Love is an international organization centered around the appreciation of computer science research papers. There's so much we can learn from the landmark research that shaped the field and the current studies that are shaping our future. Our goal is to create a community of tech professionals passionate about learning and sharing knowledge. Come join us!
New to research papers? Watch The Refreshingly Rewarding Realm of Research Papers (https://www.youtube.com/watch?v=8eRx5Wo3xYA) by Sean Cribbs.
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// Tentative Schedule
• 7:00-7:15–Eat food and talk
• 7:15-7:20–Introduction and announcements
• 7:20-8:20–A local perspective on community structure in multilayer networks (https://arxiv.org/abs/1510.05185), presented by Dr. Mandi Traud
• 8:40+–Informal paper discussion
Excella Consulting Arlington Tech Exchange (https://www.excella.com/events/arlington-tech-exchange)
2300 Wilson Blvd
Arlington, VA 22201
This month, Excella Consulting is hosting us at the Arlington Tech Exchange. It's located conveniently off Wilson Blvd in Arlington. There's parking available, and it's just a quick walk from the Courthouse Metro Station. We'll be on the 6th floor; follow the signs.
If you're late, we totally understand–please still come! Just be sure to slip in quietly if a speaker is presenting.
A local perspective on community structure in multilayer networks by Lucas G. S. Jeub, et al.
pdf (https://arxiv.org/pdf/1510.05185.pdf) | site (https://arxiv.org/abs/1510.05185)
Abstract: The analysis of multilayer networks is among the most active areas of network science, and there are now several methods to detect dense "communities" of nodes in multilayer networks. One way to define a community is as a set of nodes that trap a diffusion-like dynamical process (usually a random walk) for a long time. In this view, communities are sets of nodes that create bottlenecks to the spreading of a dynamical process on a network. We analyze the local behavior of different random walks on multiplex networks (which are multilayer networks in which different layers correspond to different types of edges) and show that they have very different bottlenecks that hence correspond to rather different notions of what it means for a set of nodes to be a good community. This has direct implications for the behavior of community-detection methods that are based on these random walks.
About Mandi: Mandi is a Senior Data Scientist at Cybraics (https://cybraics.com/); in the community, she is the co-Founder of Women Data Scientists DC (https://twitter.com/WomenDataSci/) and President of Data Community DC (https://twitter.com/DataCommunityDC/). She is a network scientist, cyber enthusiast, and cupcake queen. Find her on Twitter at @altraud (https://twitter.com/altraud).