13 citations · 33 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
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-…
Effects of Graph Convolutions in Multi-layer Networks
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Graph Convolutional Networks (GCNs) are one of the most popular architectures that are used to solve classification problems accompanied by graphical information. We present a rigo…
Graph Attention Retrospective
Kimon Fountoulakis, Amit Levi, Shenghao Yang +2
Graph-based learning is a rapidly growing sub-field of machine learning with applications in social networks, citation networks, and bioinformatics. One of the most popular models…
Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization
Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Recently there has been increased interest in semi-supervised classification in the presence of graphical information. A new class of learning models has emerged that relies, at it…