17 citations · 37 across the 5 of their papers we have counts for
5 papers
Recommendation Fairness in Social Networks Over Time
Meng Cao, Hussain Hussain, Sandipan Sikdar +3
In social recommender systems, it is crucial that the recommendation models provide equitable visibility for different demographic groups, such as gender or race. Most existing res…
Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks
Hussain Hussain, Meng Cao, Sandipan Sikdar +4
We present evidence for the existence and effectiveness of adversarial attacks on graph neural networks (GNNs) that aim to degrade fairness. These attacks can disadvantage a partic…
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +3
Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approaches are typically informed, i.e. they have access…
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +2
Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. I…
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
Tomislav Duricic, Hussain Hussain, Emanuel Lacic +3
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly a…