DDAI - Explainable, Verifiable, and Privacy-Preserving Data-Driven AI

Jan 1, 2020

DDAI Project - Privacy-Preserving, Explainable and Verifiable AI

Project Overview (January 2020 – December 2023)

The DDAI Comet Module is a research initiative that integrates privacy-preserving, explainable, and verifiable techniques into data-driven AI systems. Funded through the COMET programme (Competence Centers for Excellent Technologies), the project combines machine learning, cryptography, and interpretability methods to deliver secure and understandable AI solutions.

The project involves collaboration between the Know Center Research, Graz University of Technology, and numerous international academic and industrial partners. Additional details can be found on the DDAI project website.

My Contribution

My PhD research was partly funded by the DDAI project, enabling me to focus on explainability in recommender systems, graph-based machine learning, and modeling user behavior and preferences. My work specifically involved:

Researching methods for enhancing interpretability and transparency in recommender systems.
Evaluating novel approaches to graph embeddings and graph neural networks.
Modeling user behavior and preferences to improve the accuracy and relevance of recommendations, particularly exploring aspects such as homophily and diversity.
Collaborative research within interdisciplinary teams.

I authored several key publications and contributed significantly to additional studies within the project. Please find the full publication list below.

Explainable AI Machine Learning Graph Neural Networks Recommender Systems User Modeling Privacy-Preserving AI

Tomislav Đuričić

Researcher / Machine Learning Engineer / Software Engineer

My research interests include social-based recommender systems, graph neural networks and user modeling.

Publications

Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings

Uptrendz is a multi-domain recommendation platform offering real-time, API-centric recommendations across varied applications. Demonstrated with both MovieLens-100k movie recommendations and entrepreneurial start-up founding use cases, it distinguishes between item and system-level domains for flexible customization, deployment and evaluation. Code and documentation available on GitHub.

Emanuel Lacić, Tomislav Đuričić, Leon Fadljević, Dieter Theiler, Dominik Kowald

Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings

My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music

This study examines homophily on Last.fm based on users’ preferences for mainstream, novel, or diverse music content. We compare friendship connections to listening profiles and evaluate these features for link prediction. Results show that friends share similar artist preferences, with diversity being a stronger predictor of friendship than mainstream or novelty preferences. While high-novelty users show strong homophily, they have lower artist profile similarity. Mainstream/novel/diverse features perform comparably to artist profiles in link prediction, with combined features yielding best results—though adding no value when graph-based features are available. These insights inform music recommendation, user modeling, and cold-start link prediction.

Tomislav Đuričić, Dominik Kowald, Markus Schedl, Elisabeth Lex

My friends also prefer diverse music: homophily and link prediction with user preferences for mainstream, novelty, and diversity in music

The interplay between communities and homophily in semi-supervised classification using graph neural networks

This study investigates how community structure and homophily influence Graph Neural Networks (GNNs) in semi-supervised node classification tasks. By systematically modifying eight datasets and measuring GNN performance with and without these structural properties, we demonstrate their significant impact on classification accuracy and reveal insights about their interaction. We introduce an information-theoretic metric to quantify community-label correlation, providing practical guidelines for model selection based on graph structure. Our findings enhance understanding of GNN capabilities and improve model selection for semi-supervised node classification tasks.

Hussain Hussain, Tomislav Đuričić, Elisabeth Lex, Denis Helić, Roman Kern

The interplay between communities and homophily in semi-supervised classification using graph neural networks

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

This study introduces natural language explanations designed to promote more thoughtful reasoning about online videos and increase awareness of potentially biased or misleading content. We developed an end-to-end pipeline that extracts reflection triggers about video sources, topics, emotions, and sentiment to help users actively evaluate video usefulness. Our between-subjects study examining controversial topics reveals that users’ alignment with video messages significantly impacts perceived usefulness. While explanations were rated highly, they didn’t alter overall usefulness perceptions compared to videos alone. Notably, users with extreme negative alignment found videos less useful regardless of explanations and were more confident in their assessments. We interpret these findings through cognitive dissonance theory and propose design implications for explanations aimed at raising awareness about online deception.

Oana Inel, Tomislav Đuričić, Harmanpreet Kaur, Elisabeth Lex, Nava Tintarev

Design Implications for Explanations: A Case Study on Supporting Reflective Assessment of Potentially Misleading Videos

Structack: Structure-based Adversarial Attacks on Graph Neural Networks

Graph neural networks (GNNs) are vulnerable to adversarial attacks, but most attack methods require node attribute information. This paper introduces Structack, an uninformed attack strategy that exploits only graph structural properties without needing node attributes. By targeting links between nodes with low similarity and low centrality, Structack approaches the effectiveness of informed attacks while being computationally more efficient. The findings demonstrate that structural knowledge alone can significantly degrade GNN performance, contributing to the development of more robust graph-based machine learning methods.

Hussain Hussain, Tomislav Đuričić, Elisabeth Lex, Denis Helić, Markus Strohmaier, Roman Kern

Structack: Structure-based Adversarial Attacks on Graph Neural Networks

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

This study examines three unsupervised dimensionality reduction techniques (PCA, UMAP, and VAEs) for toxicology classification tasks. The research compares these embedding methods against standard molecular fingerprint models and explores transfer learning by training embedders on external chemical compound datasets. By testing various embedding dimensions and external dataset sizes, the findings demonstrate that UMAP can effectively complement established techniques like PCA and VAE for pre-compression in toxicology. However, VAE’s generative approach shows superior performance in pre-compression for classification accuracy.

Mario Lovrić, Tomislav Đuričić, Han T.N. Tran, Hussain Hussain, Emanuel Lacić, Morten A. Rasmussen, Roman Kern

Should We Embed in Chemistry? A Comparison of Unsupervised Transfer Learning with PCA, UMAP, and VAE on Molecular Fingerprints

Cross-platform analysis of user comments in YouTube videos linked on Reddit’s conspiracy theory forum

This study examines how language in YouTube comments changes after videos are shared on Reddit’s conspiracy forum. Analyzing 859 videos and 180,000+ comments, we found YouTube comments adopt linguistic patterns similar to r/conspiracy after Reddit exposure, evidenced by reduced text distribution distances. While sentiment remains unchanged, vocabulary measurably shifts toward conspiracy forum discourse, demonstrating cross-platform influence on user language.

Tomislav Đuričić, Volker Seiser, Elisabeth Lex

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

This study examines how community structures in graphs affect Graph Neural Networks (GNNs) in node classification tasks. Through experiments on six datasets, researchers found that community structures significantly impact GNN performance. When nodes in a community mostly share the same label, disrupting the community structure dramatically reduces performance. Conversely, when labels don’t correlate with communities, graph structure becomes less relevant and simple feature-based models perform comparably. The research provides insights and guidelines for selecting appropriate models based on graph structure characteristics.

Hussain Hussain, Tomislav Đuričić, Elisabeth Lex, Roman Kern, Denis Helić

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

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

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 available datasets, we evaluate approaches from four method families - (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.

Tomislav Đuričić, Hussain Hussain, Emanuel Lacić, Dominik Kowald, Denis Helić, Elisabeth Lex

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering