8 citations · 24 across the 25 of their papers we have counts for
10 papers · 1 filter
Finite-Time Node Separation in Recurrent Graph Neural Networks with Persistent Gaussian Perturbations
Mostafa Haghir Chehreghani
Persistent Gaussian perturbations have been shown to prevent asymptotic oversmoothing in recurrent Graph Neural Networks (GNNs) by ensuring a positive stationary Dirichlet energy.…
Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
Mostafa Haghir Chehreghani
Oversmoothing is a fundamental limitation of deep graph neural networks (GNNs), where repeated message passing causes node representations to become increasingly similar, eventuall…
On the Complexity of Optimal Graph Rewiring for Oversmoothing and Oversquashing in Graph Neural Networks
Mostafa Haghir Chehreghani
Graph Neural Networks (GNNs) face two fundamental challenges when scaled to deep architectures: oversmoothing, where node representations converge to indistinguishable vectors, and…
Graph Neural Networks in Multi-Omics Cancer Research: A Structured Survey
Payam Zohari, Mostafa Haghir Chehreghani
The task of data integration for multi-omics data has emerged as a powerful strategy to unravel the complex biological underpinnings of cancer. Recent advancements in graph neural…
Anchor-based oversampling for imbalanced tabular data via contrastive and adversarial learning
Hadi Mohammadi, Ehsan Nazerfard, Mostafa Haghir Chehreghani
Imbalanced data represent a distribution with more frequencies of one class (majority) than the other (minority). This phenomenon occurs across various domains, such as security, m…
Neighborhood-Order Learning Graph Attention Network for Fake News Detection
Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri
Fake news detection is a significant challenge in the digital age, which has become increasingly important with the proliferation of social media and online communication networks.…
A Decision-Based Heterogenous Graph Attention Network for Multi-Class Fake News Detection
Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri
A promising tool for addressing fake news detection is Graph Neural Networks (GNNs). However, most existing GNN-based methods rely on binary classification, categorizing news as ei…
LOSS-GAT: Label Propagation and One-Class Semi-Supervised Graph Attention Network for Fake News Detection
Batool Lakzaei, Mostafa Haghir Chehreghani, Alireza Bagheri
In the era of widespread social networks, the rapid dissemination of fake news has emerged as a significant threat, inflicting detrimental consequences across various dimensions of…
Hierarchical Correlation Clustering and Tree Preserving Embedding
Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani
We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters applicable to both positive and negative…
Sublinear Update Time Randomized Algorithms for Dynamic Graph Regression
Mostafa Haghir Chehreghani
A well-known problem in data science and machine learning is {\em linear regression}, which is recently extended to dynamic graphs. Existing exact algorithms for updating the solut…