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Hasselt University

Academic institutioneurope · be
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Research library22linked papers
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Selected work

Representative Papers

The Boolean Power of ReLU

Aug 12, 2026

This work investigates whether the ReLU activation function strictly outperforms eventually constant activations—such as truncated ReLU—in expressing Boolean queries within graph neural networks (GNNs). By introducing two formal languages, ReLU-MPLang and Σ-MPLang, the study characterizes the classes of queries expressible over finite undirected graphs with Boolean node features. Leveraging tools from logical expressivity, GNN theory, and formal language analysis, and combining real-coefficient linear combinations with ReLU nonlinearity, the authors rigorously establish—for the first time—that ReLU-MPLang is strictly more expressive than any Σ-MPLang built from eventually constant activation functions. This result demonstrates that ReLU-GNNs are provably superior to {TrReLU, id}-GNNs for Boolean query tasks, revealing ReLU’s unique expressive power in discrete reasoning and resolving a longstanding open problem in the field.

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Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

Jul 10, 2026

This study addresses the computational expense of calculating betweenness and closeness centrality for nodes in graphs by reframing the problem as a ranking task and proposing an efficient approximation method based on message-passing graph neural networks. Trained on a diverse mixture of synthetic graphs—including Erdős–Rényi, Barabási–Albert, and Gaussian random partition models—the model demonstrates strong generalization across unseen graph structures, notably enhancing the transferability of betweenness centrality estimates. The work also highlights the sensitivity of closeness centrality to community structure as a key challenge. Experimental results show that the model achieves Kendall’s τ of 0.851 for betweenness and 0.894 for closeness on unseen Erdős–Rényi graphs, and scales effectively to large graphs (N=5000) with τ=0.938 for betweenness, while accelerating inference by up to 97.7× compared to exact computation.

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SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting

Jul 06, 2026

This work addresses the challenge that screen-space artifacts in real-world images—such as lens smudges and UI watermarks—are often erroneously reconstructed as floating 3D objects, severely degrading novel view synthesis quality. To resolve this, we propose the first unsupervised framework that jointly optimizes a 3D Gaussian point cloud and a learnable 2D overlay layer. By leveraging multi-view geometric consistency, our method automatically disentangles static artifacts from genuine 3D structure without requiring manual annotations or prior knowledge. Evaluated on both synthetic and real-world datasets, the approach achieves up to a 9 dB PSNR improvement over the original 3D Gaussian Splatting baseline, significantly enhancing reconstruction fidelity while accurately preserving artifact content.

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Recent publications

Latest Papers

The Boolean Power of ReLU

Aug 12, 2026

This work investigates whether the ReLU activation function strictly outperforms eventually constant activations—such as truncated ReLU—in expressing Boolean queries within graph neural networks (GNNs). By introducing two formal languages, ReLU-MPLang and Σ-MPLang, the study characterizes the classes of queries expressible over finite undirected graphs with Boolean node features. Leveraging tools from logical expressivity, GNN theory, and formal language analysis, and combining real-coefficient linear combinations with ReLU nonlinearity, the authors rigorously establish—for the first time—that ReLU-MPLang is strictly more expressive than any Σ-MPLang built from eventually constant activation functions. This result demonstrates that ReLU-GNNs are provably superior to {TrReLU, id}-GNNs for Boolean query tasks, revealing ReLU’s unique expressive power in discrete reasoning and resolving a longstanding open problem in the field.

0 citationsRead paper

Graph Neural Networks for Scalable and Transferable Node Centrality Approximation

Jul 10, 2026

This study addresses the computational expense of calculating betweenness and closeness centrality for nodes in graphs by reframing the problem as a ranking task and proposing an efficient approximation method based on message-passing graph neural networks. Trained on a diverse mixture of synthetic graphs—including Erdős–Rényi, Barabási–Albert, and Gaussian random partition models—the model demonstrates strong generalization across unseen graph structures, notably enhancing the transferability of betweenness centrality estimates. The work also highlights the sensitivity of closeness centrality to community structure as a key challenge. Experimental results show that the model achieves Kendall’s τ of 0.851 for betweenness and 0.894 for closeness on unseen Erdős–Rényi graphs, and scales effectively to large graphs (N=5000) with τ=0.938 for betweenness, while accelerating inference by up to 97.7× compared to exact computation.

0 citationsRead paper

SSA-3DGS: Unsupervised Removal of Screen-Space Artifacts for 3D Gaussian Splatting

Jul 06, 2026

This work addresses the challenge that screen-space artifacts in real-world images—such as lens smudges and UI watermarks—are often erroneously reconstructed as floating 3D objects, severely degrading novel view synthesis quality. To resolve this, we propose the first unsupervised framework that jointly optimizes a 3D Gaussian point cloud and a learnable 2D overlay layer. By leveraging multi-view geometric consistency, our method automatically disentangles static artifacts from genuine 3D structure without requiring manual annotations or prior knowledge. Evaluated on both synthetic and real-world datasets, the approach achieves up to a 9 dB PSNR improvement over the original 3D Gaussian Splatting baseline, significantly enhancing reconstruction fidelity while accurately preserving artifact content.

0 citationsRead paper