Events
Past Event
WED@NICO WEBINAR: Maksim Kitsak, Delft University of Technology
Northwestern Institute on Complex Systems (NICO)
12:00 PM
Details
Speaker:
Maksim Kitsak, Assistant Professor, Electrical Engineering, Mathematics and Computer Science, Delft University of Technology
Title:
Geometric Representations of Complementarity-Driven Networks
Abstract:
Similarity is one of the key principles underlying the formation of social networks: the more similar individuals are the higher is the chance for a social interaction between them. Latent geometry provides an elegant way to model similarity in social networks. Network nodes are viewed as points in underlying latent or hidden space, such that distances between them quantify node similarities: the smaller the distance between the two nodes the more similar they are. It is the similarity interpretation of latent distances that lies at the origin of many applications of network embeddings, including link prediction, soft community detection and clustering, network navigation, and search.
In my talk, however, I will focus on another class of networks that are shaped not only by similarity but also by the complementarity principle. Examples of complementarity-driven networks include interdisciplinary collaboration networks, networks of interacting proteins, and, possibly, food webs. Indeed, individuals with complementary expertise are more likely to solve an interdisciplinary problem of interest, and interactions often take place between proteins with complementary chemical properties and/or complementary binding interfaces. One of the most popular food web network models, the niche model, is based not on the similarity but on the complementarity principle, as I will demonstrate.
I will argue that existing network embedding methods are not readily applicable to complementarity-driven networks. I will then deduce a proper framework for the representations of complementarity-driven networks and demonstrate its efficiency in network reconstruction tasks.
Speaker Bio:
Maksim Kitsak is an assistant professor in the faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology. Dr. Kitsak earned his Ph.D. in theoretical physics from Boston University. Dr. Kitsak has held postdoctoral positions at the Center for Applied Internet Data Analysis (CAIDA), UC San Diego; and the Center for Complex Network Research (CCNR), Northeastern University. His research focuses on the development of theoretical and computational approaches to networked systems, with diverse applications ranging from systems biology to civil infrastructure. Results of his research have been published in top cross-disciplinary journals, such as Nature, Nature Physics, Science, and Science Advances, and have received broad media coverage.
Webinar:
Webinar link: https://northwestern.zoom.us/j/91064172482
Passcode: nico
ID: 910 6417 2482
About the Speaker Series:
Wednesdays@NICO is a vibrant weekly seminar series focusing broadly on the topics of complex systems and data science. It brings together attendees ranging from graduate students to senior faculty who span all of the schools across Northwestern, from applied math to sociology to biology and every discipline in-between. Please visit: https://bit.ly/WedatNICO for information on future speakers.
Time
Wednesday, January 20, 2021 at 12:00 PM - 1:00 PM
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
Data Science Nights - MAY 2026 - Speaker: Xudong Tang, Computer Science and NICO
Northwestern Institute on Complex Systems (NICO)
5:30 PM
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M416, Technological Institute
Details
MAY MEETING: Thursday, May 28, 2026 at 5:30pm (US Central)
LOCATION:
ESAM Conference Room, Tech M416
2145 Sheridan Road, Evanston, IL 60208
AGENDA:
5:30pm - Meet and greet with refreshments
6:00pm - Talk with Xudong Tang, PhD Student, Computer Science, NICO, and the Human-AI Collaboration Lab, Northwestern University
TALK TITLE:
Human and Machine Perception of Voice Similarity
ABSTRACT:
Modern voice cloning systems generate synthetic speech that listeners frequently cannot identify as being synthetic. But a voice can sound natural without sounding like the intended person, and what determines whether a clone is heard as a particular person is an open question. Here we report a large-scale preregistered experiment in which we collected 92,239 responses from 175 participants on their perception of pairs of real recordings, voice clones, and continuously morphed voices drawn from 100 contemporary celebrities across 20 speaker groups. We find that voice clones do not reliably preserve perceived speaker identity, reducing same-speaker judgments by 12.7 percentage points even though the clones are produced by a state-of-the-art text-to-speech model, while leaving different-speaker judgments unchanged. Using continuously morphed stimuli, we find that speakers vary substantially in how much variation their perceived identity tolerates, and that this variation is not predicted by speaker demographics. Speaker embeddings account for 58.9\% (95\% CI = [55.7, 61.9]) of variance in identity judgments, which is more than acoustic features, social attributes, and clone status combined. Once all these observed features are accounted for, clone status adds no additional predictive power. These results shows that the perceptual impact of voice cloning is positional rather than categorical: we can model how listeners judge a voice by how close it falls to the perceptual boundary that defines each speaker's recognizable voice, applying the same criterion to real and synthetic speech alike.
DATA SCIENCE NIGHTS are monthly meetings featuring presentations and discussions about data-driven science and complex systems, organized by Northwestern University graduate students and scholars. Students and researchers of all levels are welcome! For more information: http://bit.ly/nico-dsn
FUTURE DATES:
Data Science Nights will return in September!
Time
Thursday, May 28, 2026 at 5:30 PM - 7:00 PM
Location
M416, Technological Institute Map
Contact
Calendar
Northwestern Institute on Complex Systems (NICO)
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Time
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Time
Friday, June 19, 2026
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Fall 2026 Classes Begin
Time
Wednesday, September 23, 2026
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