3 papers
math.ST2026
The Value of Depth in Message Passing on Sparse Graphs: A Kesten-Stigum Dichotomy
Aseem Raj Baranwal
How deep does a graph neural network need to be on a sparse graph? We study its purest statistical form: node classification on the sparse contextual stochastic block model (CSBM)…
cs.LG2025
Optimality of Message-Passing Architectures for Sparse Graphs
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
We study the node classification problem on feature-decorated graphs in the sparse setting, i.e., when the expected degree of a node is in the number of nodes, in the fixed-…
cs.LG2024
Analysis of Corrected Graph Convolutions
Robert Wang, Aseem Baranwal, Kimon Fountoulakis
Machine learning for node classification on graphs is a prominent area driven by applications such as recommendation systems. State-of-the-art models often use multiple graph convo…