IEEE

Robust Finite-Time Stabilization of Fractional-Order Neural Networks With Discontinuous and Continuous Activation Functions Under Uncertainty

Abstract: This paper is concerned with robust finite-time stabilization for a class of fractional-order neural networks (FNNs) with two types of activation functions (i.e., discontinuous and ...
IEEE

Fundamental Limits of Deep Graph Convolutional Networks for Graph Classification

Abstract: Graph convolutional networks (GCNs) are a widely used method for graph representation learning. To elucidate their capabilities and limitations for graph classification, we investigate their ...
Microsoft

Graph Limits and Parameter Testing

We define a distance of two graphs that reflects the closeness of both local and global properties. We also define convergence of a sequence of graphs, and show that a graph sequence is convergent if ...
Microsoft

An Lp theory of sparse graph convergence I: limits, sparse random graph models, and power law distributions

We introduce and develop a theory of limits for sequences of sparse graphs based on Lp graphons, which generalizes both the existing L1 theory of dense graph limits and its extension by Bollob as and ...
GitHub

Bayesian Optimization of Functions over Node Subsets in Graphs [NeurIPS2024]

This is the repo for GraphComBO, a Bayesian optimization tool for black-box and expensive functions defined over node subsets in graphs, where the goal is to find the optimal subset within a limited ...
GitHub

Continuous Query Decomposition

This repository contains the official implementation for our ICLR 2021 (Oral, Outstanding Paper Award) paper, Complex Query Answering with Neural Link Predictors: In this work we present CQD, a method ...