First Online Convening, June 2020

Instead of meeting for a second time at the University of Pittsburgh in Summer 2020, the global Covid-19 pandemic required us to move our previously planned, two-week, in-person, co-working initiative to a virtual convening format. During the week of June 22, 2020, we designed and offered a series of virtual events that allowed the NA+DAH teams to present their current progress, receive feedback from the whole community, and also to design future plans in light of the pandemic. Our focus was on collecting and consolidating the teams’ thoughts, re-convening as a community, and learning from one another. 

Online convenings can be difficult. With this fact clearly in mind, we produced a gathering that aimed to take full advantage of the in-person relationships that had already been forged during our Summer 2019 convening in Pittsburgh, as well as the ongoing conversations that had been supported by our webinar series and virtual project check-in meetings that had taken place between August 2019 and June 2020. All but one of the teams was able to shift their plans to accommodate the move to an online modality. 

One of the central pressures on extended, virtual convenings, we believe, is a perceived inefficiency. Therefore, to scaffold this work and to try to take as full advantage of our limited synchronous time together as possible, we asked participants to submit white papers well in advance of the meeting. All members of our community were then asked to read these white papers and come prepared to our virtual convening to discuss the work of their peers. To read the guidance we offered to the teams when preparing these documents, please click here.

But our efforts to focus our community’s attention during our synchronous sessions did not stop at our request for these written project updates, we also grouped the teams into “feedback pairings.” These pairs of teams were asked to pay special attention to one another’s white papers and to prepare 10-minute presentations of their feedback to be offered synchronously during our Zoom sessions. Important to this peer feedback plan was the intentionality behind the pairings of the projects that were chosen to engage directly with one another.

By scheduling time for prepared peer feedback directly into the virtual convening, and scaffolding it with pre-circulated white papers available to all, we were able to use our time together directly discussing the projects rather than needing to take time catching up on our current status. Also worth noting is that, by concluding the week with an incredible keynote from the network analyst Tina Eliassi-Rad, the community was galvanized to action by her expert guidance as we parted ways. The participants were prepared by our week together to ask Eliassi-Rad direct questions about issues they already knew were shared across teams and she was able to effectively offer guidance to the entire community.

To view the original schedule, feel free to click here.

Keynote Synopsis

  • Tina Eliassi-Rad, “The Reasonable Effectiveness of Roles in Complex Networks”
    • Given a graph, how can we automatically discover roles of nodes? Roles compactly represent structural behaviors of nodes and generalize across various graphs. Examples of roles include “clique-members,” “periphery-nodes,” “bridges,” etc… Are there good features that we can extract for nodes that indicate role-membership? How are roles different from communities and from equivalences (from sociology)? What are the applications in which these discovered roles can be effectively used? In this talk, Eliassi-Rad addressed these questions, provide dalgorithms for role discovery, and discussed why roles are so effective in many applications from transfer learning to re-identification to anomaly detection to mining time-evolving networks and multi-relational graphs.


Tina Eliassi-Rad is a Professor of Computer Science at Northeastern University. She is also a core faculty member at Northeastern’s Network Science Institute and the Institute for Experiential AI. Her research is at the intersection of data mining, machine learning, and network science. She has over 100 peer-reviewed publications (including a few best paper and best paper runner-up awards); and has given over 200 invited talks and 14 tutorials.