Intelligent Systems
Note: This research group has relocated.

3 results (View BibTeX file of all listed publications)

2022

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Modeling Node Exposure for Community Detection in Networks

Othman, S., Schulz, J., Baity-Jesi, M., De Bacco, C.

Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2022, pages: 233-244 , Studies in Computational Intelligence , Springer, 11th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2021), July 2022 (conference)

Preprint link (url) DOI [BibTex]

2022

Othman, S., Schulz, J., Baity-Jesi, M., De Bacco, C. Modeling Node Exposure for Community Detection in Networks Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and their Applications: COMPLEX NETWORKS 2022, pages: 233-244 , Studies in Computational Intelligence , Springer, 11th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2021), July 2022 (conference)

Preprint link (url) DOI [BibTex]

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Estimating Social Influence from Observational Data

Sridhar, D., De Bacco, C., Blei, D.

First Conference on Causal Learning and Reasoning, 2022 (conference)

Preprint link (url) [BibTex]

Sridhar, D., De Bacco, C., Blei, D. Estimating Social Influence from Observational Data First Conference on Causal Learning and Reasoning, 2022 (conference)

Preprint link (url) [BibTex]

2021

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Convergence properties of optimal transport-based temporal networks

Baptista Theuerkauf, D., De Bacco, C.

Complex Networks & Their Applications X , 1, Studies in Computational Intelligence , 10th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2021) , September 2021 (conference)

Abstract
We study network properties of networks evolving in time based on optimal transport principles. These evolve from a structure covering uniformly a continuous space towards an optimal design in terms of optimal transport theory. At convergence, the networks should optimize the way resources are transported through it. As the network structure shapes in time towards optimality, its topological properties also change with it. The question is how do these change as we reach optimality. We study the behavior of various network properties on a number of network sequences evolving towards optimal design and find that the transport cost function converges earlier than network properties and that these monotonically decrease. This suggests a mechanism for designing optimal networks by compressing dense structures. We find a similar behavior in networks extracted from real images of the networks designed by the body shape of a slime mold evolving in time.

Preprint DOI [BibTex]

2021

Baptista Theuerkauf, D., De Bacco, C. Convergence properties of optimal transport-based temporal networks Complex Networks & Their Applications X , 1, Studies in Computational Intelligence , 10th International Conference on Complex Networks and Their Applications (COMPLEX NETWORKS 2021) , September 2021 (conference)

Preprint DOI [BibTex]