Merge pull request #3 from danielskatz/patch-1

minor changes in paper and bib
lmiconsulting · Dec 13, 2022 · e8420c6 · e8420c6
2 parents 0e6b909 + 70130c1
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diff --git a/paper/paper.md b/paper/paper.md
@@ -49,7 +49,7 @@ Given an undirected graph $G=(V, E)$ with vertex set $V$ and edge set $E$, a dif
 
 The infection subgraph $I_t$ is the subgraph of $G$ induced by the infected vertices at time $t$. In the single-source SI model, $I_t$ is guaranteed to be connected. A common setting for source localization is to infer $S$ from some $I_t$. More recently, some techniques have incorporated information from a small set of observers, who record the time at which they become infected [@zhu2016locating].
 
-Broadly speaking, source estimators fall into one of two categories: message-passing algorithms, such as *Short-Fat Tree* [@zhu2014information], or spectral algorithms, such as *NETSLEUTH* [@prakash2012spotting]. An extensive overview of source localization techniques is provided by Ying and Zhu [@ying2018diffusion].
+Broadly speaking, source estimators fall into one of two categories: message-passing algorithms, such as *Short-Fat Tree* [@zhu2014information], or spectral algorithms, such as *NETSLEUTH* [@prakash2012spotting]. An extensive overview of source localization techniques is provided by @ying2018diffusion.
 
 
 

diff --git a/paper/references.bib b/paper/references.bib
@@ -77,7 +77,7 @@ @article{frkaszczak2022rpasdt
 }
 
 @article{karczmarczyk2021oonis,
-  title={OONIS — Onis—Object-Oriented Network Infection Simulator},
+  title={OONIS --- Object-Oriented Network Infection Simulator},
   author={Karczmarczyk, Artur and Jankowski, Jaros{\l}aw and W{\k{a}}tr{\'o}bski, Jaros{\l}aw},
   journal={SoftwareX},
   volume={14},
@@ -88,11 +88,14 @@ @article{karczmarczyk2021oonis
 }
 
 @article{miller2020eon,
-  title={Eon (Epidemics on Networks): A Fast, Flexible Python Package for Simulation, Analytic Approximation, and Analysis of Epidemics on Networks},
+  title={{EoN} (Epidemics on Networks): A Fast, Flexible {P}ython Package for Simulation, Analytic Approximation, and Analysis of Epidemics on Networks},
   author={Miller, Joel C and Ting, Tony},
-  journal={arXiv preprint arXiv:2001.02436},
-  year={2020},
-  doi={10.21105/joss.01731}
+  journal={Journal of Open Source Software},
+  year={2019},
+  doi={10.21105/joss.01731},
+  volume={4},
+  number={44},
+  pages={1731},
 }
 
 @software{lucas_mccabe_2021_4456181,
@@ -107,16 +110,17 @@ @software{lucas_mccabe_2021_4456181
 }
 
 @article{jenness2018epimodel,
-  title={EpiModel: Epimodel: An R Package For Mathematical Modeling of Infectious Disease Over Networks},
+  title={EpiModel: An R Package For Mathematical Modeling of Infectious Disease Over Networks},
   author={Jenness, Samuel M and Goodreau, Steven M and Morris, Martina},
-  journal={Journal of statistical software},
+  journal={Journal of Statistical Software},
   volume={84},
   year={2018},
-  publisher={NIH Public Access}
+  publisher={NIH Public Access},
+  doi={10.18637/jss.v084.i08},
 }
 
 @article{rossetti2018ndlib,
-  title={NDlib: A Python Library to Model and Analyze Diffusion Processes Over Complex Networks},
+  title={NDlib: A {P}ython Library to Model and Analyze Diffusion Processes Over Complex Networks},
   author={Rossetti, Giulio and Milli, Letizia and Rinzivillo, Salvatore and S{\^\i}rbu, Alina and Pedreschi, Dino and Giannotti, Fosca},
   journal={International Journal of Data Science and Analytics},
   volume={5},