The graph represents a network of 136 Twitter users whose tweets in the requested range contained "#FutureNursing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 08 November 2019 at 19:15 UTC.
The requested start date was Wednesday, 06 November 2019 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000.
The tweets in the network were tweeted over the 96-day, 6-hour, 28-minute period from Thursday, 01 August 2019 at 13:53 UTC to Tuesday, 05 November 2019 at 20:21 UTC.
Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".
The graph is directed.
The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Author Description
Vertices : 136
Unique Edges : 216
Edges With Duplicates : 96
Total Edges : 312
Number of Edge Types : 3
Mentions : 285
Tweet : 14
Replies to : 13
Self-Loops : 14
Reciprocated Vertex Pair Ratio : 0.0990990990990991
Reciprocated Edge Ratio : 0.180327868852459
Connected Components : 17
Single-Vertex Connected Components : 3
Maximum Vertices in a Connected Component : 86
Maximum Edges in a Connected Component : 252
Maximum Geodesic Distance (Diameter) : 5
Average Geodesic Distance : 2.843297
Graph Density : 0.0132897603485839
Modularity : 0.53887
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 136 Twitter users whose tweets in the requested range contained "#FutureNursing", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 08 November 2019 at 19:15 UTC.
The requested start date was Wednesday, 06 November 2019 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000.
The tweets in the network were tweeted over the 96-day, 6-hour, 28-minute period from Thursday, 01 August 2019 at 13:53 UTC to Tuesday, 05 November 2019 at 20:21 UTC.
Additional tweets that were mentioned in this data set were also collected from prior time periods. These tweets may expand the complete time period of the data.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, and a self-loop edge for each tweet that is not a "replies-to" or "mentions".
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : GraphServerTwitterSearch
Graph Term : #FutureNursing
Groups : The graph's vertices were grouped by cluster using the Clauset-Newman-Moore cluster algorithm.
Edge Color : Edge Weight
Edge Width : Edge Weight
Edge Alpha : Edge Weight
Vertex Radius : Followers
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[12] spare,30 [12] 30,mins [12] mins,month [12] month,help [11] #gpn,spare [11] help,make [11] make,sure [11] sure,busy [11] busy,#generalpractice [11] #generalpractice,nurses Top Word Pairs in Tweet in G1:
[4] dtacademylondon,bootcamp [4] rnajimboblandi,bevmatthewsrn [4] bevmatthewsrn,zcarciente [4] crimpolene,lecturer [4] lecturer,adult [4] adult,nurse [4] nurse,middlesexuni [4] middlesexuni,doing [4] doing,student [4] student,workshops Top Word Pairs in Tweet in G2:
[6] dtacademylondon,bootcamp [3] edgar,degas [3] degas,art [3] art,see [3] see,make [3] make,others [3] others,see [3] see,sculpture [3] sculpture,see [3] see,nurse Top Word Pairs in Tweet in G3:
[12] spare,30 [12] 30,mins [12] mins,month [12] month,help [11] #gpn,spare [11] help,make [11] make,sure [11] sure,busy [11] busy,#generalpractice [11] #generalpractice,nurses Top Word Pairs in Tweet in G4:
[5] datt_colette,amelia_tickell [5] amelia_tickell,wgs_science [5] wgs_science,phe_uk [5] phe_uk,shanetickell [5] shanetickell,roylilley [5] roylilley,muziekgeluk [5] muziekgeluk,gbtpo [5] gbtpo,kennygibsonnhs [5] kennygibsonnhs,nhswomen [5] nhswomen,nhsengland Top Word Pairs in Tweet in G5:
[6] amazing,device [6] device,stitches [6] stitches,up [6] up,w [6] w,stictches [6] stictches,#medicaltech [6] #medicaltech,#healthtech [6] #healthtech,#futurenursing [6] #futurenursing,alvinfoo [5] machinelearntec,amazing Top Word Pairs in Tweet in G6:
[3] well,good [3] good,luck [3] luck,stevie [3] stevie,tna [3] tna,course [3] course,u [3] u,greatly [3] greatly,missed [3] missed,isherwood [3] isherwood,ward Top Word Pairs in Tweet in G7:
[3] bring,#healthcare [3] #healthcare,silicon [3] silicon,valley [3] valley,bring [3] bring,#siliconvalley [3] #siliconvalley,healthcare [2] digitalmeddoc,bring [2] healthcare,#digital Top Word Pairs in Tweet in G8:
[4] playing,nurses [4] nurses,boys [4] boys,nephews [4] nephews,#futurenursing [4] #futurenursing,cnoengland [4] cnoengland,#teamcno [4] #teamcno,apparently [4] apparently,broken [4] broken,leg [3] lisavogwill,playing Top Word Pairs in Tweet in G10:
[3] nursing,midwifery [2] midwifery,followers [2] followers,please [2] please,sign [2] sign,up [2] up,ambassador [2] ambassador,transforming [2] transforming,perception [2] perception,nursing Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
Top Replied-To in G3:
Top Replied-To in G9:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
Top Mentioned in G3:
Top Mentioned in G4:
Top Mentioned in G5:
Top Mentioned in G6:
Top Mentioned in G7:
Top Mentioned in G8:
Top Mentioned in G9:
Top Mentioned in G10:
Top Tweeters in Entire Graph:
Top Tweeters in G1:
Top Tweeters in G2:
Top Tweeters in G3:
Top Tweeters in G4:
Top Tweeters in G5:
Top Tweeters in G6:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: