Recommendation Fairness in Social Networks Over Time

no code implementations • 5 Feb 2024 • Meng Cao, Hussain Hussain, Sandipan Sikdar, Denis Helic, Markus Strohmaier, Roman Kern

We further study how interventions on network properties influence fairness by examining counterfactual scenarios with alternative evolution outcomes and differing network properties.

counterfactual Fairness +1

Adversarial Inter-Group Link Injection Degrades the Fairness of Graph Neural Networks

1 code implementation • 13 Sep 2022 • Hussain Hussain, Meng Cao, Sandipan Sikdar, Denis Helic, Elisabeth Lex, Markus Strohmaier, Roman Kern

We hope our findings raise awareness about this issue in our community and lay a foundation for the future development of GNN models that are more robust to such attacks.

Fairness Node Classification

Structack: Structure-based Adversarial Attacks on Graph Neural Networks

1 code implementation • 23 Jul 2021 • Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Denis Helic, Markus Strohmaier, Roman Kern

In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes.

On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks

1 code implementation • 30 Oct 2020 • Hussain Hussain, Tomislav Duricic, Elisabeth Lex, Roman Kern, Denis Helic

In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs.

Classification General Classification +2

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

no code implementations • 30 Mar 2020 • Tomislav Duricic, Hussain Hussain, Emanuel Lacic, Dominik Kowald, Denis Helic, Elisabeth Lex

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering.

Collaborative Filtering Network Embedding