Selim Haj Ali

Phone: +49 152 57 58 54 98
Mail: s.hajali (at) jacobs-university.de
Position: Ph.D. student

Research

I am part of the i-CONN Network, a Marie Skłodowska-Curie ITN project funded by the European Commission. Within this uniquely interdisciplinary environment, I investigate the theoretical underpinnings of pattern formation on graphs.

The goal is to search for evidence of these collective behaviors in real-world networked systems, explain their emergence, determine their stability and predict regime shifts, thus, providing a holistic understanding of emergent collective phenomena on graphs and ultimately, facilitate the use of this knowledge in several application scenarios present within Connectivity Science.

My work involves combining tools from network science, dynamical systems theory and statistics.

Recent Publications

Inferring missing edges in a graph from observed collective patterns
Selim Haj Ali , Marc-Thorsten Hütt
Physical Review E, 2022.
Cite

Cite

                    @article{hajaliInferringMissingEdges2022,
 abstract = {Many real-life networks are incomplete. Dynamical observations can allow estimating missing edges. Such procedures, often summarized under the term ‘network inference’, typically evaluate the statistical correlations among pairs of nodes to determine connectivity. Here, we offer an alternative approach: completing an incomplete network by observing its collective behavior. We illustrate this approach for the case of patterns emerging in reaction-diffusion systems on graphs, where collective behaviors can be associated with eigenvectors of the network's Laplacian matrix. Our method combines a partial spectral decomposition of the network's Laplacian matrix with eigenvalue assignment by matching the patterns to the eigenvectors of the incomplete graph. We show that knowledge of a few collective patterns can allow the prediction of missing edges and that this result holds across a range of network architectures. We present a numerical case study using activator-inhibitor dynamics and we illustrate that the main requirement for the observed patterns is that they are not confined to subsets of nodes, but involve the whole network.},
 author = {Haj Ali, Selim and Hütt, Marc-Thorsten},
 doi = {10.1103/PhysRevE.105.064610},
 file = {APS Snapshot:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/HS5VIBKF/PhysRevE.105.html:text/html;Full Text PDF:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/I3PDVLD8/Haj Ali und Hütt - 2022 - Inferring missing edges in a graph from observed c.pdf:application/pdf},
 journal = {Physical Review E},
 month = {June},
 note = {Publisher: American Physical Society},
 number = {6},
 pages = {064610},
 title = {Inferring missing edges in a graph from observed collective patterns},
 url = {https://link.aps.org/doi/10.1103/PhysRevE.105.064610},
 urldate = {2022-09-09},
 volume = {105},
 year = {2022}
}