From 0813f08b56b92d75b97291fc3a192d076d9a10d7 Mon Sep 17 00:00:00 2001 From: Michael Thomas Schaub Date: Sun, 28 Jul 2024 10:02:19 +0200 Subject: [PATCH] updates --- index.md | 1 + publications.bib | 36 +++++++++-------- publications.md | 102 ++++++++++++++++++++++++----------------------- 3 files changed, 73 insertions(+), 66 deletions(-) diff --git a/index.md b/index.md index 253e192..45f5942 100644 --- a/index.md +++ b/index.md @@ -18,6 +18,7 @@ We are always looking for excellent PhD students and PostDocs. If you are inter ### News and Events +***July 22-26, 2024*** -- I am attending the Newton Institute workshop "Hypergraphs: Theory and Applications" at the Alan Turing Institute. If you are around, feel free to get in touch. ***June 7, 2024*** -- Many cool new papers from my group uploaded to the arxiv in the last days -- check them out here: [\[Paper1\]](https://arxiv.org/abs/2406.02997)[\[Paper2\]](https://arxiv.org/abs/2406.02300)[\[Paper3\]](https://arxiv.org/abs/2406.02269)[\[Paper4\]](https://arxiv.org/abs/2406.01999). ***June 3, 2024*** -- I am happy to announce that I have been selected as a member of the European Laboratory for Learning and Intelligent Systems (ELLIS). ***May 9, 2024*** -- Our [paper](https://www.science.org/doi/10.1126/sciadv.adh4053) with Leonie, Michael Scholkemper and Francisco Tudisco on learning the dynamics on hypergraphs is now out in Science Advances! diff --git a/publications.bib b/publications.bib index 9725ae2..ad186c8 100644 --- a/publications.bib +++ b/publications.bib @@ -273,17 +273,17 @@ @Article{Billeh2018 url = {https://arxiv.org/abs/1701.04905}, } -@Article{Segarra2017, - author = {Segarra, Santiago and Schaub, Michael T. and Jadbabaie, Ali}, - journal = {56th IEEE Conference on Decision and Control (CDC 2017)}, - title = {Network Inference from Consensus Dynamics}, - year = {2017}, - month = dec, - pages = {3212--3217}, - abstract = {We consider the problem of identifying the topology of a weighted, undirected network G from observing snapshots of multiple independent consensus dynamics. Specifically, we observe the opinion profiles of a group of agents for a set of M independent topics and our goal is to recover the precise relationships between the agents, as specified by the unknown network G. In order to overcome the under- determinacy of the problem at hand, we leverage concepts from spectral graph theory and convex optimization to unveil the underlying network structure. More precisely, we formulate the network inference problem as a convex optimization that seeks to endow the network with certain desired properties – such as sparsity – while being consistent with the spectral information extracted from the observed opinions. This is complemented with theoretical results proving consistency as the number M of topics grows large. We further illustrate our method by numerical experiments, which showcase the effectiveness of the technique in recovering synthetic and real-world networks.}, - doi = {10.1109/CDC.2017.8264130}, - owner = {mschaub}, - url = {https://arxiv.org/abs/1708.05329}, +@InProceedings{Segarra2017, + author = {Segarra, Santiago and Schaub, Michael T. and Jadbabaie, Ali}, + booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)}, + title = {Network Inference from Consensus Dynamics}, + year = {2017}, + month = dec, + pages = {3212--3217}, + abstract = {We consider the problem of identifying the topology of a weighted, undirected network G from observing snapshots of multiple independent consensus dynamics. Specifically, we observe the opinion profiles of a group of agents for a set of M independent topics and our goal is to recover the precise relationships between the agents, as specified by the unknown network G. In order to overcome the under- determinacy of the problem at hand, we leverage concepts from spectral graph theory and convex optimization to unveil the underlying network structure. More precisely, we formulate the network inference problem as a convex optimization that seeks to endow the network with certain desired properties – such as sparsity – while being consistent with the spectral information extracted from the observed opinions. This is complemented with theoretical results proving consistency as the number M of topics grows large. We further illustrate our method by numerical experiments, which showcase the effectiveness of the technique in recovering synthetic and real-world networks.}, + doi = {10.1109/CDC.2017.8264130}, + owner = {mschaub}, + url = {https://arxiv.org/abs/1708.05329}, } @Article{Avella-Medina2020, @@ -859,14 +859,18 @@ @Article{Calmon2023 url = {https://arxiv.org/abs/2301.10137}, } -@Misc{Arnaudon2023, +@Article{Arnaudon2024, author = {Alexis Arnaudon and Dominik J Schindler and Robert L Peach and Adam Gosztolai and Maxwell Hodges and Michael T Schaub and Mauricio Barahona}, - howpublished = {submitted}, - month = mar, - title = {PyGenStability: Multiscale community detection with generalized Markov Stability}, - year = {2023}, + journal = {ACM Transactions on Mathematical Software}, + title = {Algorithm 1044: PyGenStability: Multiscale community detection with generalized Markov Stability}, + year = {2024}, + issn = {0098-3500}, + month = jun, abstract = {We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden algorithms. The package includes automatic detection of robust graph partitions and allows the flexibility to choose quality functions for weighted undirected, directed and signed graphs, and to include other user-defined quality functions. The code and documentation are hosted on GitHub under a GNU General Public License at this https URL.}, + address = {New York, NY, USA}, creationdate = {2023-03-15}, + doi = {10.1145/3651225}, + publisher = {Association for Computing Machinery}, url = {https://arxiv.org/abs/2303.05385}, } diff --git a/publications.md b/publications.md index 97521d7..b95f15c 100644 --- a/publications.md +++ b/publications.md @@ -38,8 +38,30 @@ A list of my publications is also available on [Google Scholar](https://scholar. + + + + + + + + + - @@ -58,7 +80,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -77,7 +99,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -97,7 +119,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -116,7 +138,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -136,7 +158,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -160,7 +182,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -180,7 +202,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -202,7 +224,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -224,7 +246,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -244,7 +266,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -266,7 +288,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -286,7 +308,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -306,7 +328,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -326,7 +348,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -348,7 +370,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -370,7 +392,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -394,7 +416,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -418,7 +440,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -445,7 +467,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -469,7 +491,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -493,7 +515,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -517,7 +539,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -541,7 +563,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -567,7 +589,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -590,7 +612,7 @@ A list of my publications is also available on [Google Scholar](https://scholar. - @@ -608,26 +630,6 @@ A list of my publications is also available on [Google Scholar](https://scholar. } - - - - - - - - - - @@ -1468,10 +1470,10 @@ A list of my publications is also available on [Google Scholar](https://scholar.
[1] Arnaudon, A.; Schindler, D.J.; Peach, R.L.; Gosztolai, A.; Hodges, M.; Schaub, M.T. & Barahona, M. (2024), "Algorithm 1044: PyGenStability: Multiscale community detection with generalized Markov Stability", ACM Transactions on Mathematical Software. New York, NY, USA, June, 2024. Association for Computing Machinery. +

[Abstract] [BibTeX] [DOI] [URL]

+
Abstract: We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden algorithms. The package includes automatic detection of robust graph partitions and allows the flexibility to choose quality functions for weighted undirected, directed and signed graphs, and to include other user-defined quality functions. The code and documentation are hosted on GitHub under a GNU General Public License at this https URL.
BibTeX: + 
+@article{Arnaudon2024,
+    author = {Alexis Arnaudon and Dominik J Schindler and Robert L Peach and Adam Gosztolai and Maxwell Hodges and Michael T Schaub and Mauricio Barahona},
+           title = {Algorithm 1044: PyGenStability: Multiscale community detection with generalized Markov Stability},
+           journal = {ACM Transactions on Mathematical Software},
+           publisher = {Association for Computing Machinery},
+           year = {2024},
+           url = {https://arxiv.org/abs/2303.05385},
+           doi = {10.1145/3651225}
+}
+
[1] Epping B, René A, Helias M and Schaub MT (2024), "Graph Neural Networks Do Not Always Oversmooth". June, 2024. + [2] Epping B, René A, Helias M and Schaub MT (2024), "Graph Neural Networks Do Not Always Oversmooth". June, 2024.

[Abstract] [BibTeX] [URL]
[2] Grande VP and Schaub MT (2024), "Node-Level Topological Representation Learning on Point Clouds". June, 2024. + [3] Grande VP and Schaub MT (2024), "Node-Level Topological Representation Learning on Point Clouds". June, 2024.

[Abstract] [BibTeX] [URL]
[3] Hoppe J and Schaub MT (2024), "Random Abstract Cell Complexes". June, 2024. + [4] Hoppe J and Schaub MT (2024), "Random Abstract Cell Complexes". June, 2024.

[Abstract] [BibTeX] [URL]
[4] Scholkemper M, Wu X, Jadbabaie A and Schaub M (2024), "Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs". June, 2024. + [5] Scholkemper M, Wu X, Jadbabaie A and Schaub M (2024), "Residual Connections and Normalization Can Provably Prevent Oversmoothing in GNNs". June, 2024.

[Abstract] [BibTeX] [URL]
[5] Telyatnikov L, Bernardez G, Montagna M, Vasylenko P, Zamzmi G, Hajij M, Schaub MT, Miolane N, Scardapane S and Papamarkou T (2024), "TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning". June, 2024. + [6] Telyatnikov L, Bernardez G, Montagna M, Vasylenko P, Zamzmi G, Hajij M, Schaub MT, Miolane N, Scardapane S and Papamarkou T (2024), "TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning". June, 2024.

[Abstract] [BibTeX] [URL]
[6] Neuhäuser, L.; Scholkemper, M.; Tudisco, F. & Schaub, M.T. (2024), "Learning the effective order of a hypergraph dynamical system", Science Advances., May, 2024. Vol. 10(19), pp. eadh4053. + [7] Neuhäuser, L.; Scholkemper, M.; Tudisco, F. & Schaub, M.T. (2024), "Learning the effective order of a hypergraph dynamical system", Science Advances., May, 2024. Vol. 10(19), pp. eadh4053.

[Abstract] [BibTeX] [DOI] [URL]
[7] Frantzen, F. & Schaub, M.T. (2024), "Learning From Simplicial Data Based on Random Walks and 1D Convolutions", In Interational Conference on Learning Representations., April, 2024. + [8] Frantzen, F. & Schaub, M.T. (2024), "Learning From Simplicial Data Based on Random Walks and 1D Convolutions", In Interational Conference on Learning Representations., April, 2024.

[Abstract] [BibTeX] [URL]
[8] Scholkemper, M.; Kühn, D.; Nabbefeld, G.; Musall, S.; Kampa, B. & Schaub, M.T. (2024), "A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9751-9755. + [9] Scholkemper, M.; Kühn, D.; Nabbefeld, G.; Musall, S.; Kampa, B. & Schaub, M.T. (2024), "A Wasserstein Graph Distance Based on Distributions of Probabilistic Node Embeddings", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9751-9755.

[Abstract] [BibTeX] [DOI] [URL]
[9] Grande, V. & Schaub, M.T. (2024), "Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9896-9900. + [10] Grande, V. & Schaub, M.T. (2024), "Disentangling the Spectral Properties of the Hodge Laplacian: Not All Small Eigenvalues Are Equal", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9896-9900.

[Abstract] [BibTeX] [DOI] [URL]
[10] Fanuel M, Aspeel A, Schaub MT and Delvenne J-C (2024), "Ellipsoidal embeddings of graphs". March, 2024. + [11] Fanuel M, Aspeel A, Schaub MT and Delvenne J-C (2024), "Ellipsoidal embeddings of graphs". March, 2024.

[Abstract] [BibTeX] [URL]
[11] Nagai, J.S.; Costa, I.G. & Schaub, M.T. (2024), "Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9856-9860. + [12] Nagai, J.S.; Costa, I.G. & Schaub, M.T. (2024), "Optimal transport distances for directed, weighted graphs: a case study with cell-cell communication networks", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024)., March, 2024. , pp. 9856-9860.

[Abstract] [BibTeX] [DOI] [URL]
[12] Papamarkou T, Birdal T, Bronstein M, Carlsson G, Curry J, Gao Y, Hajij M, Kwitt R, Liò P, Lorenzo PD, Maroulas V, Miolane N, Nasrin F, Ramamurthy KN, Rieck B, Scardapane S, Schaub MT, Veličković P, Wang B, Wang Y, Wei G-W and Zamzmi G (2024), "Position Paper: Challenges and Opportunities in Topological Deep Learning", arxiv. February, 2024. + [13] Papamarkou T, Birdal T, Bronstein M, Carlsson G, Curry J, Gao Y, Hajij M, Kwitt R, Liò P, Lorenzo PD, Maroulas V, Miolane N, Nasrin F, Ramamurthy KN, Rieck B, Scardapane S, Schaub MT, Veličković P, Wang B, Wang Y, Wei G-W and Zamzmi G (2024), "Position Paper: Challenges and Opportunities in Topological Deep Learning", arxiv. February, 2024.

[Abstract] [BibTeX] [URL]
[13] Hajij M, Papillon M, Frantzen F, Agerberg J, AlJabea I, Ballester R, Battiloro C, Bernárdez G, Birdal T, Brent A, Chin P, Escalera S, Fiorellino S, Gardaa OH, Gopalakrishnan G, Govil D, Hoppe J, Karri MR, Khouja J, Lecha M, Livesay N, Meißner J, Mukherjee S, Nikitin A, Papamarkou T, Prílepok J, Ramamurthy KN, Rosen P, Guzmán-Sáenz A, Salatiello A, Samaga SN, Scardapane S, Schaub MT, Scofano L, Spinelli I, Telyatnikov L, Truong Q, Walters R, Yang M, Zaghen O, Zamzmi G, Zia A and Miolane N (2024), "TopoX: A Suite of Python Packages for Machine Learning on Topological Domains", arxiv. February, 2024. + [14] Hajij M, Papillon M, Frantzen F, Agerberg J, AlJabea I, Ballester R, Battiloro C, Bernárdez G, Birdal T, Brent A, Chin P, Escalera S, Fiorellino S, Gardaa OH, Gopalakrishnan G, Govil D, Hoppe J, Karri MR, Khouja J, Lecha M, Livesay N, Meißner J, Mukherjee S, Nikitin A, Papamarkou T, Prílepok J, Ramamurthy KN, Rosen P, Guzmán-Sáenz A, Salatiello A, Samaga SN, Scardapane S, Schaub MT, Scofano L, Spinelli I, Telyatnikov L, Truong Q, Walters R, Yang M, Zaghen O, Zamzmi G, Zia A and Miolane N (2024), "TopoX: A Suite of Python Packages for Machine Learning on Topological Domains", arxiv. February, 2024.

[Abstract] [BibTeX] [URL]
[14] Stamm FI and Schaub MT (2024), "Faster optimal univariate microgaggregation", arxiv. January, 2024. + [15] Stamm FI and Schaub MT (2024), "Faster optimal univariate microgaggregation", arxiv. January, 2024.

[Abstract] [BibTeX] [URL]
[15] Scholkemper, M. & Schaub, M.T. (2023), "An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", In Advances in Neural Information Processing Systems (NeurIPS 2023)., December, 2023. Vol. 36, pp. 71358-71374. + [16] Scholkemper, M. & Schaub, M.T. (2023), "An Optimization-based Approach To Node Role Discovery in Networks: Approximating Equitable Partitions", In Advances in Neural Information Processing Systems (NeurIPS 2023)., December, 2023. Vol. 36, pp. 71358-71374.

[Abstract] [BibTeX] [URL]
[16] Hajij, M.; Zamzmi, G.; Papamarkou, T.; Guzmán-Sáenz, A.; Birdal, T. & Schaub, M.T. (2023), "Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs", In 57th Asilomar Conference on Signals, Systems, and Computers., December, 2023. , pp. 799-803. + [17] Hajij, M.; Zamzmi, G.; Papamarkou, T.; Guzmán-Sáenz, A.; Birdal, T. & Schaub, M.T. (2023), "Combinatorial Complexes: Bridging the Gap Between Cell Complexes and Hypergraphs", In 57th Asilomar Conference on Signals, Systems, and Computers., December, 2023. , pp. 799-803.

[Abstract] [BibTeX] [DOI] [URL]
[17] Grande, V.P. & Schaub, M.T. (2023), "Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 17:1-17:19. PMLR. + [18] Grande, V.P. & Schaub, M.T. (2023), "Non-Isotropic Persistent Homology: Leveraging the Metric Dependency of PH", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 17:1-17:19. PMLR.

[Abstract] [BibTeX] [URL]
[18] Loveland, D.; Zhu, J.; Heimann, M.; Fish, B.; Schaub, M.T. & Koutra, D. (2023), "On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 6:1-6:30. PMLR. + [19] Loveland, D.; Zhu, J.; Heimann, M.; Fish, B.; Schaub, M.T. & Koutra, D. (2023), "On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 6:1-6:30. PMLR.

[Abstract] [BibTeX] [URL]
[19] Hoppe, J. & Schaub, M.T. (2023), "Representing Edge Flows on Graphs via Sparse Cell Complexes", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 1:1-1:22. PMLR. + [20] Hoppe, J. & Schaub, M.T. (2023), "Representing Edge Flows on Graphs via Sparse Cell Complexes", In Proceedings of the Second Learning on Graphs Conference., November, 2023. Vol. 231, pp. 1:1-1:22. PMLR.

[Abstract] [Comment] [BibTeX] [URL]
[20] Calmon, L.; Schaub, M.T. & Bianconi, G. (2023), "Dirac signal processing of higher-order topological signals", New Journal of Physics., September, 2023. Vol. 25(9), pp. 093013. + [21] Calmon, L.; Schaub, M.T. & Bianconi, G. (2023), "Dirac signal processing of higher-order topological signals", New Journal of Physics., September, 2023. Vol. 25(9), pp. 093013.

[Abstract] [BibTeX] [DOI] [URL]
[21] Bick, C.; Gross, E.; Harrington, H.A. & Schaub, M.T. (2023), "What are higher-order networks?", SIAM Review., August, 2023. Vol. 65(3), pp. 686-731. + [22] Bick, C.; Gross, E.; Harrington, H.A. & Schaub, M.T. (2023), "What are higher-order networks?", SIAM Review., August, 2023. Vol. 65(3), pp. 686-731.

[Abstract] [BibTeX] [DOI] [URL]
[22] Grande, V.P. & Schaub, M.T. (2023), "Topological Point Cloud Clustering", In Proceedings of the 40th International Conference on Machine Learning (ICML 2023)., July, 2023. Vol. 202, pp. 11683-11697. PMLR. + [23] Grande, V.P. & Schaub, M.T. (2023), "Topological Point Cloud Clustering", In Proceedings of the 40th International Conference on Machine Learning (ICML 2023)., July, 2023. Vol. 202, pp. 11683-11697. PMLR.

[Abstract] [BibTeX] [URL]
[23] Schaub, M.T.; Li, J. & Peel, L. (2023), "Hierarchical community structure in networks", Phys. Rev. E., May, 2023. Vol. 107, pp. 054305. American Physical Society. + [24] Schaub, M.T.; Li, J. & Peel, L. (2023), "Hierarchical community structure in networks", Phys. Rev. E., May, 2023. Vol. 107, pp. 054305. American Physical Society.

[Abstract] [BibTeX] [DOI] [URL]
[24] Neuhäuser, L.; Karimi, F.; Bachmann, J.; Strohmaier, M. & Schaub, M.T. (2023), "Improving the visibility of minorities through network growth interventions", Communication Physics., May, 2023. Vol. 6(108) + [25] Neuhäuser, L.; Karimi, F.; Bachmann, J.; Strohmaier, M. & Schaub, M.T. (2023), "Improving the visibility of minorities through network growth interventions", Communication Physics., May, 2023. Vol. 6(108)

[Abstract] [Comment] [BibTeX] [DOI] [URL]
[25] Stamm, F.I.; Scholkemper, M.; Strohmaier, M. & Schaub, M.T. (2023), "Neighborhood Structure Configuration Models", In The Web Conference. New York, NY, USA, April, 2023. , pp. 210–220. Association for Computing Machinery. + [26] Stamm, F.I.; Scholkemper, M.; Strohmaier, M. & Schaub, M.T. (2023), "Neighborhood Structure Configuration Models", In The Web Conference. New York, NY, USA, April, 2023. , pp. 210–220. Association for Computing Machinery.

[Abstract] [BibTeX] [DOI] [URL]
[26] Hajij M, Zamzmi G, Papamarkou T, Miolane N, Guzmán-Sáenz A, Ramamurthy KN, Birdal T, Dey TK, Mukherjee S, Samaga SN and others (2023), "Topological Deep Learning: Going Beyond Graph Data". April, 2023. + [27] Hajij M, Zamzmi G, Papamarkou T, Miolane N, Guzmán-Sáenz A, Ramamurthy KN, Birdal T, Dey TK, Mukherjee S, Samaga SN and others (2023), "Topological Deep Learning: Going Beyond Graph Data". April, 2023.

[Abstract] [BibTeX] [URL]
[27] Arnaudon A, Schindler DJ, Peach RL, Gosztolai A, Hodges M, Schaub MT and Barahona M (2023), "PyGenStability: Multiscale community detection with generalized Markov Stability", submitted. March, 2023. -

[Abstract] [BibTeX] [URL]

-
Abstract: We present PyGenStability, a general-use Python software package that provides a suite of analysis and visualisation tools for unsupervised multiscale community detection in graphs. PyGenStability finds optimized partitions of a graph at different levels of resolution by maximizing the generalized Markov Stability quality function with the Louvain or Leiden algorithms. The package includes automatic detection of robust graph partitions and allows the flexibility to choose quality functions for weighted undirected, directed and signed graphs, and to include other user-defined quality functions. The code and documentation are hosted on GitHub under a GNU General Public License at this https URL.
BibTeX: -
-@misc{Arnaudon2023,
-  author = {Alexis Arnaudon and Dominik J Schindler and Robert L Peach and Adam Gosztolai and Maxwell Hodges and Michael T Schaub and Mauricio Barahona},
-  title = {PyGenStability: Multiscale community detection with generalized Markov Stability},
-  howpublished = {submitted},
-  year = {2023},
-  url = {https://arxiv.org/abs/2303.05385}
-}
-
[28] Roddenberry, T.M.; Grande, V.P.; Frantzen, F.; Schaub, M.T. & Segarra, S. (2023), "Signal Processing on Product Spaces", In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., March, 2023. , pp. 1-5. IEEE.

[Abstract] [BibTeX] [DOI] [URL]

@@ -1458,7 +1460,7 @@ A list of my publications is also available on [Google Scholar](https://scholar.
[64] Segarra, S.; Schaub, M.T. & Jadbabaie, A. (2017), "Network Inference from Consensus Dynamics", 56th IEEE Conference on Decision and Control (CDC 2017)., December, 2017. , pp. 3212-3217. + [64] Segarra, S.; Schaub, M.T. & Jadbabaie, A. (2017), "Network Inference from Consensus Dynamics", In 56th IEEE Conference on Decision and Control (CDC 2017)., December, 2017. , pp. 3212-3217.

[Abstract] [BibTeX] [DOI] [URL]
BibTeX: 
-@article{Segarra2017,
+@inproceedings{Segarra2017,
     author = {Segarra, Santiago and Schaub, Michael T. and Jadbabaie, Ali},
            title = {Network Inference from Consensus Dynamics},
-           journal = {56th IEEE Conference on Decision and Control (CDC 2017)},
+           booktitle = {56th IEEE Conference on Decision and Control (CDC 2017)},
            year = {2017},
            pages = {3212--3217},
            url = {https://arxiv.org/abs/1708.05329},
@@ -1862,5 +1864,5 @@ A list of my publications is also available on [Google Scholar](https://scholar.