8 citations · 24 across the 25 of their papers we have counts for
28 papers
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…
AI Models for Depressive Disorder Detection and Diagnosis: A Review
Dorsa Macky Aleagha, Payam Zohari, Mostafa Haghir Chehreghani
Major Depressive Disorder is one of the leading causes of disability worldwide, yet its diagnosis still depends largely on subjective clinical assessments. Integrating Artificial I…
PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
Mohammad Naeimi, Mostafa Haghir Chehreghani
Recommender systems based on graph neural networks (GNNs) have been proved to perform well on user-item interactions. However, they commonly suffer from popularity bias -- the tend…
Contrastive Matrix Completion with Denoising and Augmented Graph Views for Robust Recommendation
Narges Nemati, Mostafa Haghir Chehreghani
Matrix completion is a widely adopted framework in recommender systems, as predicting the missing entries in the user-item rating matrix enables a comprehensive understanding of us…
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…
Adaptive Long-term Embedding with Denoising and Augmentation for Recommendation
Zahra Akhlaghi, Mostafa Haghir Chehreghani
The rapid growth of the internet has made personalized recommendation systems indispensable. Graph-based sequential recommendation systems, powered by Graph Neural Networks (GNNs),…
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.…
Disentangling Popularity and Quality: An Edge Classification Approach for Fair Recommendation
Nemat Gholinejad, Mostafa Haghir Chehreghani
Graph neural networks (GNNs) have proven to be an effective tool for enhancing the performance of recommender systems. However, these systems often suffer from popularity bias, lea…
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…
Heterophily-Aware Fair Recommendation using Graph Convolutional Networks
Nemat Gholinejad, Mostafa Haghir Chehreghani
In recent years, graph neural networks (GNNs) have become a popular tool to improve the accuracy and performance of recommender systems. Modern recommender systems are not only des…
Strong Transitivity Relations and Graph Neural Networks
Yassin Mohamadi, Mostafa Haghir Chehreghani
Local neighborhoods play a crucial role in embedding generation in graph-based learning. It is commonly believed that nodes ought to have embeddings that resemble those of their ne…
Multilevel User Credibility Assessment in Social Networks
Mohammad Moradi, Mostafa Haghir Chehreghani
Online social networks serve as major platforms for disseminating both real and fake news. Many users--intentionally or unintentionally--spread harmful content, misinformation, and…
The Embeddings World and Artificial General Intelligence
Mostafa Haghir Chehreghani
From early days, a key and controversial question inside the artificial intelligence community was whether Artificial General Intelligence (AGI) is achievable. AGI is the ability o…
Effectively Counting s-t Simple Paths in Directed Graphs
Mostafa Haghir Chehreghani
An important tool in analyzing complex social and information networks is s-t simple path counting, which is known to be #P-complete. In this paper, we study efficient s-t simple p…
Generalized Centrality Aggregation and Exclusive Centrality
Mostafa Haghir Chehreghani
There are several applications that benefit from a definition of centrality which is applicable to sets of vertices, rather than individual vertices. However, existing definitions…
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…
Novel Adaptive Algorithms for Estimating Betweenness, Coverage and k-path Centralities
Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
An important index widely used to analyze social and information networks is betweenness centrality. In this paper, first given a directed network and a vertex , we…
Exact and Approximate Algorithms for Computing Betweenness Centrality in Directed Graphs
Mostafa Haghir Chehreghani, Albert Bifet, Talel Abdessalem
Graphs (networks) are an important tool to model data in different domains. Real-world graphs are usually directed, where the edges have a direction and they are not symmetric. Bet…
Metropolis-Hastings Algorithms for Estimating Betweenness Centrality in Large Networks
Mostafa Haghir Chehreghani, Talel Abdessalem, and Albert Bifet
Betweenness centrality is an important index widely used in different domains such as social networks, traffic networks and the world wide web. However, even for mid-size networks…