Adel Javanmard
University of Southern California, CA·Mathematics & Statistics
Networks are everywhere: friendships and political ties in society, gene regulation and protein interactions in biology, and links between users on online platforms. Finding the hidden groups, or communities, inside these networks makes the data easier to interpret and compress.
- Fields
Mathematics & Statistics
Computer Science
- Topics
network data
community detection
clustering
- Grants
- 1 known grant, through Jul 2027
What they work on
- Design clustering algorithms based on semidefinite programming, a form of mathematical optimization, that combine high-dimensional side information on each node with the network's interaction graph to detect communities.
- Develop community detection methods that remain accurate under adversarial perturbations, meaning deliberate corruptions of either the node-level contextual data or the interaction graph.
- Build low-computation, memory-efficient versions of these clustering algorithms that scale to large, sparse networks, then explore their use across diverse application domains.
More about the research
It can also reveal hidden subpopulations that need separate models and help fill in missing labels. Many clustering algorithms already exist, but they often scale poorly to large data or break down when the data are noisy or manipulated. Network data also often come with extra information about each node, such as a user's demographics or browsing history. Combining this information with the network itself could improve clustering.
Why it matters
Reliable, scalable community detection could help researchers and engineers understand and manage complex network systems. Examples include social networks, gene regulation and protein interaction networks, and online platforms. Methods that stay accurate under corrupted data and scale to very large graphs could make clustering more dependable in real-world use.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Marianna Pensky
University of Central Florida, FL·Mathematics & Statistics
Extend the multiplex Generalized Dot Product Graph model so that groups of network layers can have different underlying structures. This would allow layers to be clustered by condition.
Weijing Tang
Carnegie Mellon University, PA·Mathematics & Statistics
Develop priority-aware rules for valuing training data based on the Shapley value, a game-theory method for fairly splitting credit, that account for originality, provenance and individual risk within a unified axiomatic framework.
Paul Valiant
Purdue University, IN·Computer Science
Design error-correcting codes that withstand random or adversarial insertion and deletion errors while supporting local decoding, which recovers part of a message by reading only a small piece of the stored data.
The school
University of Southern California: 47 labs on PI Pages, $1.3B a year on research, 882 research doctorates a year.
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