Caleb Belth
Hello! I'm Caleb, a PhD candidate at the University of Michigan, where
I work with Danai Koutra.
Some of my interests are streaming algorithms, graph summarization, information theory, and philosophy.
My research leverages ideas from information theory and linguistics
to contribute methods to represent and utilize graphs that are missing data ("data mining with limited data").
Examples include evolving networks, which only contain the network's past evolution (the future is "missing"),
sensor networks that do not have complete measurement coverage, and knowledge graphs that are missing facts about the world.
I try my best to be responsible with my research, and continually consider whether my work will be constructive or destructive to society.
Prior to Michigan, I received a B.Sc. in Computer Science from Purdue University, where I was fortunate
to work with
Jennifer Neville,
Dan Goldwasser, and
Daisuke Kihara.
Feel free to contact me at cbelth@umich.edu.
In case you are wondering, the name of this website ("quickshift") is a nod to my life-long love of road trips,
driving, and cars in general.
Publications
-
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization.
Caleb Belth, Xinyi (Carol) Zheng, Jilles Vreeken, Danai Koutra
ACM The Web Conference (WWW), April 2020 (oral presentation, acceptance rate 19%) [pdf] [code] -
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket
Tara Safavi, Caleb Belth, Lukas Faber, Davide Mottin, Emmanuel Muller, Danai Koutra
IEEE International Conference on Data Mining (ICDM), 2019 (acceptance rate: 9%) [pdf] -
When to Remember Where You Came from: Node Representation Learning in Higher-order Networks
Caleb Belth, Fahad Kamran, Donna Tjandra, Danai Koutra
IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2019 (acceptance rate: 15%) [pdf] [slides]
Also accepted for presentation at the 15th SIGKDD International Workshop on Mining and Learning with Graphs.
Essays
