most citedSequential Graph Neural Networks for Source Code Vulnerability Identification

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2024

Control-based Graph Embeddings with Data Augmentation for Contrastive Learning

Obaid Ullah Ahmad, Anwar Said, Mudassir Shabbir +2

In this paper, we study the problem of unsupervised graph representation learning by harnessing the control properties of dynamical networks defined on graphs. Our approach introdu…

cs.SI2023

Enhanced Graph Neural Networks with Ego-Centric Spectral Subgraph Embeddings Augmentation

Anwar Said, Mudassir Shabbir, Tyler Derr +2

Graph Neural Networks (GNNs) have shown remarkable merit in performing various learning-based tasks in complex networks. The superior performance of GNNs often correlates with the…

eess.SY2023

Controllability Backbone in Networks

Obaid Ullah Ahmad, Waseem Abbas, Mudassir Shabbir

This paper studies the controllability backbone problem in dynamical networks defined over graphs. The main idea of the controllability backbone is to identify a small subset of ed…

cs.LG2023

Learning-Based Heuristic for Combinatorial Optimization of the Minimum Dominating Set Problem using Graph Convolutional Networks

Abihith Kothapalli, Mudassir Shabbir, Xenofon Koutsoukos

A dominating set of a graph is a subset of vertices such that every vertex outside the dominating set is…

cs.CR20231 cited

Sequential Graph Neural Networks for Source Code Vulnerability Identification

Ammar Ahmed, Anwar Said, Mudassir Shabbir +1

Vulnerability identification constitutes a task of high importance for cyber security. It is quite helpful for locating and fixing vulnerable functions in large applications. Howev…