updates

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!      

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},
 }