updated paper refs for igraph

HNX_Paper/paper.bib

@@ -169,9 +169,17 @@ @book{knuth1993
   publisher={AcM Press New York}
 }
 
+@misc{antonov2023igraph,
+      title={{i}graph enables fast and robust network analysis across programming languages}, 
+      author={Michael Antonov and Gábor Csárdi and Szabolcs Horvát and Kirill Müller and Tamás Nepusz and Daniel Noom and Maëlle Salmon and Vincent Traag and Brooke Foucault Welles and Fabio Zanini},
+      year={2023},
+      eprint={2311.10260},
+      archivePrefix={arXiv},
+      primaryClass={cs.SI}
+}
 
-@article{csardi2006,
-  title={The igraph software package for complex network research},
+@article{csardi2006igraph,
+  title={The {i}graph software package for complex network research},
   author={Csardi, Gabor and Nepusz, Tamas and others},
   journal={InterJournal, complex systems},
   volume={1695},

HNX_Paper/paper.md

@@ -57,7 +57,7 @@ Metadata may be attached to the tool by providing tabular data via two optional
 
 # Statement of need
 For more than a century, graph theory has provided powerful methods for studying network relationships among abstract entities.
-Since the early 2000's, software packages such as NetworkX [@SciPyProceedings_11] and Igraph [@csardi2006]  have
+Since the early 2000's, software packages such as NetworkX [@SciPyProceedings_11] and `igraph` [@csardi2006igraph;@antonov2023igraph]  have
 made these theoretical tools available to data scientists for studying large data sets.
 Graphs represent pairwise interactions between entities, but for many network datasets this is a severe limitation.
 In 1973, hypergraphs were introduced by Claude Berge [@Berge1973Graphs] as a strict generalization of graphs: a hyperedge in a hypergraph can contain any number of nodes, including 1, 2, or more.