no code implementations • NeurIPS 2023 • 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 $O(1)$ in the number of nodes, in the fixed-dimensional asymptotic regime, i. e., the dimension of the feature data is fixed while the number of nodes is large.
no code implementations • 20 Apr 2022 • 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.
1 code implementation • 26 Feb 2022 • Kimon Fountoulakis, Amit Levi, Shenghao Yang, Aseem Baranwal, Aukosh Jagannath
They were introduced to allow a node to aggregate information from features of neighbor nodes in a non-uniform way, in contrast to simple graph convolution which does not distinguish the neighbors of a node.
2 code implementations • 13 Feb 2021 • Aseem Baranwal, Kimon Fountoulakis, Aukosh Jagannath
Recently there has been increased interest in semi-supervised classification in the presence of graphical information.
General Classification Out-of-Distribution Generalization +1