The graph represents a network of 1 263 Twitter users whose recent tweets contained "#plague", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18 000 tweets. The network was obtained from Twitter on Thursday, 14 November 2019 at 23:14 UTC.
The tweets in the network were tweeted over the 8-day, 14-hour, 46-minute period from Wednesday, 06 November 2019 at 08:15 UTC to Thursday, 14 November 2019 at 23:02 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 : 1266
Unique Edges : 1361
Edges With Duplicates : 794
Total Edges : 2155
Number of Edge Types : 4
Replies to : 116
Mentions : 815
Retweet : 900
Tweet : 324
Self-Loops : 330
Reciprocated Vertex Pair Ratio : 0,0136546184738956
Reciprocated Edge Ratio : 0,0269413629160063
Connected Components : 257
Single-Vertex Connected Components : 144
Maximum Vertices in a Connected Component : 486
Maximum Edges in a Connected Component : 534
Maximum Geodesic Distance (Diameter) : 7
Average Geodesic Distance : 2,218964
Graph Density : 0,000788016159951046
Modularity : 0,580742
NodeXL Version : 1.0.1.421
Graph Gallery URL : https://nodexlgraphgallery.org/Pages/Graph.aspx?graphID=216483
Data Import : The graph represents a network of 1 263 Twitter users whose recent tweets contained "#plague", or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18 000 tweets. The network was obtained from Twitter on Thursday, 14 November 2019 at 23:14 UTC.
The tweets in the network were tweeted over the 8-day, 14-hour, 46-minute period from Wednesday, 06 November 2019 at 08:15 UTC to Thursday, 14 November 2019 at 23:02 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 : TwitterSearch
Graph Term : #plague
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 : Betweenness Centrality
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[502] pneumonic,plague [475] china,two [461] plague,outbreak [459] two,diagnosed [459] contagious,disease [458] china,more [458] breaking,news [458] worry,pneumonic [458] confirmed,china [457] news,china Top Word Pairs in Tweet in G1:
[456] breaking,news [456] news,china [456] china,more [456] more,economy [456] economy,worry [456] worry,pneumonic [456] pneumonic,plague [456] plague,outbreak [456] outbreak,confirmed [456] confirmed,china Top Word Pairs in Tweet in G2:
[28] two,people [17] #plague,#china [12] people,diagnosed [12] pneumonic,#plague [10] diagnosed,pneumonic [9] black,death [9] pneumonic,plague [8] plague,china [7] people,#plague [7] two,cases Top Word Pairs in Tweet in G3:
[59] inner,mongolia [54] pneumonic,#plague [52] diagnosed,pneumonic [52] #plague,beijing [49] china's,inner [49] mongolia,diagnosed [44] confirmed,tuesday [42] beijing,local [41] local,health [41] health,authorities Top Word Pairs in Tweet in G4:
[33] #yersinia,pestis [27] updated,view [27] virulence,determinants [27] determinants,immune [27] immune,subversion [27] subversion,vaccination [25] pestis,#plague [25] #plague,updated [18] view,#evolution [18] #evolution,virulence Top Word Pairs in Tweet in G5:
[31] support,greatest [31] greatest,potus [31] potus,countries [31] countries,history [31] history,#demonrats [31] #demonrats,#plague [31] #plague,america [31] america,people [31] people,sick [31] sick,lies Top Word Pairs in Tweet in G6:
[31] type,thing [31] thing,look [31] look,forward [31] forward,visiting [31] visiting,angeles [31] angeles,wonder [31] wonder,#plague [31] #plague,returning [31] returning,#recallericgarcetti [31] #recallericgarcetti,#recallgavinnewsom Top Word Pairs in Tweet in G7:
[31] chinese,hospital [31] hospital,treating [31] treating,two [31] two,patients [31] patients,pneumonic [31] pneumonic,#plague Top Word Pairs in Tweet in G8:
[20] give,credit [20] credit,due [20] due,past [20] past,few [20] few,years [20] years,led [20] led,supportive [20] supportive,responses [20] responses,#plague [20] #plague,madagascar Top Word Pairs in Tweet in G9:
[29] zaraali2k19,wadood_e [28] rajasaeediqbal4,zaraali2k19 [28] wadood_e,scorpionhinar [28] scorpionhinar,nimrabu55782621 [28] nimrabu55782621,jehanzeb_waris [28] jehanzeb_waris,salehabadat13 [28] salehabadat13,aaliya28970869 [28] dreamer4927,ilyashussain67 [28] mrwebonlinenow,shaz_gujar [28] shaz_gujar,neelofer23 Top Word Pairs in Tweet in G10:
[12] marcel__keller,biorxivpreprint [9] #medievaltwitter,okay [9] okay,one [9] one,detail [9] detail,needed [9] needed,make [9] make,sense [9] sense,latest [9] latest,outbreak [9] outbreak,#plague Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G5:
Top Replied-To in G9:
Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
Top Mentioned in G5:
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: