From 63532c1d1d41ea25af9ed9400fbb91adeea6e2ce Mon Sep 17 00:00:00 2001 From: narnolddd Date: Tue, 21 Nov 2023 16:31:16 +0000 Subject: [PATCH] add pic and figure 1 rejig --- paper/joss-raphtory.bib | 2 +- paper/paper.md | 9 +-------- paper/table/Figure1.pptx | Bin 0 -> 790615 bytes paper/table/Slide1.pdf | Bin 0 -> 618887 bytes paper/table/~$Figure1.pptx | Bin 0 -> 165 bytes 5 files changed, 2 insertions(+), 9 deletions(-) create mode 100644 paper/table/Figure1.pptx create mode 100644 paper/table/Slide1.pdf create mode 100644 paper/table/~$Figure1.pptx diff --git a/paper/joss-raphtory.bib b/paper/joss-raphtory.bib index df688bd123..46f9cffc7b 100644 --- a/paper/joss-raphtory.bib +++ b/paper/joss-raphtory.bib @@ -61,7 +61,7 @@ @article{steer2020raphtory } @article{yousaf2023non, - title={Non-Markovian paths and cycles in NFT trades}, + title={Non-{Markovian} paths and cycles in NFT trades}, author={Yousaf, Haaroon and Arnold, Naomi A and Lambiotte, Renaud and LaRock, Timothy and Clegg, Richard G and Zhong, Peijie and Alnaimi, Alhamza and Steer, Ben}, journal={arXiv preprint arXiv:2303.11181}, year={2023} diff --git a/paper/paper.md b/paper/paper.md index 241137f085..5b8a1fb2b8 100644 --- a/paper/paper.md +++ b/paper/paper.md @@ -70,8 +70,6 @@ bibliography: joss-raphtory.bib # aas-doi: 10.3847/xxxxx <- update this with the DOI from AAS once you know it. # aas-journal: Astrophysical Journal <- The name of the AAS journal. --- -# Raphtory: The temporal graph engine for Rust and Python - # Summary Raphtory is a platform for building and analysing temporal networks. The library includes methods for creating networks from a variety of data sources; algorithms to explore their structure and evolution; and an extensible GraphQL server for deployment of applications built on top. Raphtory's core engine is built in Rust, for efficiency, with Python interfaces, for ease of use. Raphtory is developed by network scientists, with a background in Physics, Applied Mathematics, Engineering and Computer Science, for use across academia and industry. @@ -108,12 +106,7 @@ Raphtory includes fast and scalable implementations of algorithms for temporal n Finally, Raphtory is built with a focus on ease of use and can be installed using standard Python and Rust package managers. Once installed it can be integrated within an analysis pipeline or run standalone as a GraphQL service. - - Example code | Visualisation -:-------------------------:|:-------------------------: -![](https://hackmd.io/_uploads/rJB-cMKNT.png)|![](https://hackmd.io/_uploads/BJhzditwn.png) -![](https://hackmd.io/_uploads/S1pTufY4T.png)|![](https://hackmd.io/_uploads/ryNb_RTPh.png) -![](https://hackmd.io/_uploads/BynHFfFE6.png)|![](https://hackmd.io/_uploads/HJb3uAgv2.png) + ![](table/Slide1.pdf){width=380pt} **Caption.** First line (Example 1): In a temporal network, edges are dynamical entities connecting pairs of nodes. Second line (Example 2): Generation of a sequence of graph views at a given time resolution and on selected layers, to run standard network algorithms, here Pagerank. Third line (Example 3): Raphtory offers rapid implementations of algorithms specifically designed for temporal networks, here finding significant temporal motifs [@paranjape2017motifs]. # Projects using Raphtory diff --git a/paper/table/Figure1.pptx b/paper/table/Figure1.pptx new file mode 100644 index 0000000000000000000000000000000000000000..435dd626dc01194cb0fe6d875277563d023baf80 GIT binary patch literal 790615 zcmeFZRdi&_k|Zi-E-{ptOU%rWVpfTnnOQ2S#LUdh%*@QpQi+)vpU&xi-80i?tvmBO zJ^LdwcShvL_7!GfZtjtClAvIyKoCGsKtMo*K!^|t^$oy4K(25=K;M9%Ks1DGtR0Q4 z9d(r4Y>gbWXkD!=339%VaX#*?&Ud+AXFFQwlQC2ioTvx%@FFuXON z1&BU=wc=&3Hb`qAeRP=MAd&0}`7WoC7V%tqWX3sLES*RxAcyz-tTYrh*Gm8+Fi5cF z2p-nB%$X(eb&?{JqJTzD@gvQ6I0lrlE$D(ON#exhIJ^WZ;2;tju_|GcOx`5`Qu?Oh zijA+yaeyjR%iyu8kNI0e$GFAYoFKheQD~d9wrvG&a`gE9)`%F&(2z5YA)di#7EIM> zM+V2xApEsr=qtdZQu^FD6GtTI64dYsscvz(_Et0XdQ)Lm#$P~~HehLg{ecG4GG1qr zWC$}qFJ{%1PdW@t%aD*$?wT?XSK^kCN1;tC*b&1l3?Fm*cB)(6-9v7SXPvm8?QB|0 zdYO@I%sljGSv17owRildGC5ZNhQg=_A 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