The graph represents a network of 559 Twitter users whose recent tweets contained "#idtwitter", 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 Tuesday, 16 April 2019 at 18:17 UTC.
The tweets in the network were tweeted over the 9-day, 14-hour, 45-minute period from Sunday, 07 April 2019 at 03:19 UTC to Tuesday, 16 April 2019 at 18:04 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 : 559
Unique Edges : 1416
Edges With Duplicates : 482
Total Edges : 1898
Self-Loops : 80
Reciprocated Vertex Pair Ratio : 0.043859649122807
Reciprocated Edge Ratio : 0.0840336134453782
Connected Components : 13
Single-Vertex Connected Components : 5
Maximum Vertices in a Connected Component : 520
Maximum Edges in a Connected Component : 1797
Maximum Geodesic Distance (Diameter) : 7
Average Geodesic Distance : 3.386858
Graph Density : 0.00495957322663999
Modularity : 0.546666
NodeXL Version : 1.0.1.410
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[113] #eccmid2019,#idtwitter [108] #idtwitter,#idmeded [76] infectious,diseases [52] patients,#idtwitter [52] #cmi,escmid [52] infections,due [46] critically,ill [46] ill,patients [46] highest,number [46] number,abstracts Top Word Pairs in Tweet in G1:
[85] #idtwitter,#idmeded [79] #eccmid2019,#idtwitter [65] infectious,diseases [48] #cmi,escmid [48] infections,due [47] patients,#idtwitter [45] highest,number [45] number,abstracts [42] ironically,country [42] country,highest Top Word Pairs in Tweet in G2:
[2] keith_s_kaye,nofishman [2] nofishman,uchicagocmo [2] uchicagocmo,payal_patel [2] payal_patel,drjanpatterson [2] drjanpatterson,doctorfungus [2] h_babcock,sujitsuchindran [2] sujitsuchindran,emilymichelel [2] emilymichelel,preetinmalani [2] preetinmalani,drjootz [2] nicely,done Top Word Pairs in Tweet in G3:
[20] management,breakthrough [20] breakthrough,invasive [20] invasive,fungal [20] fungal,infections [20] infections,without [20] without,confirmed [20] confirmed,diagnoses [20] diagnoses,challenging [20] challenging,id [20] id,cases Top Word Pairs in Tweet in G4:
[12] darcy_id_doc,torontoiddoc [12] torontoiddoc,idsainfo [12] idsainfo,iddocadi [12] iddocadi,sebpoule [12] sebpoule,drjrmarcelin [12] drjrmarcelin,jdcooperid [12] jdcooperid,shohamtxid [12] shohamtxid,paulsaxmd [12] paulsaxmd,breeweaver [12] breeweaver,kabourgi Top Word Pairs in Tweet in G5:
[15] new,article [15] article,two [15] two,profs [15] profs,mk_idpharm [15] mk_idpharm,bwdionne [15] bwdionne,students [15] students,andriadangg [15] andriadangg,brpharmacist [15] brpharmacist,monipatt [15] monipatt,soontobepharmd Top Word Pairs in Tweet in G6:
[5] yeast,meets [5] meets,mold [5] mold,beauty [5] beauty,#microrounds [5] #microrounds,#idtwitter [5] #idtwitter,colleenkraftmd [5] colleenkraftmd,carlosdelrio7 [5] carlosdelrio7,janecyoon [5] janecyoon,boghuma [5] boghuma,natesummrsa Top Word Pairs in Tweet in G7:
[22] #idtwitter,#medtwitter [18] mycoplasma,genitalium [7] cdcdirector,cdcgov [5] isn,t [5] #medtwitter,#idtwitter [5] 3rd,line [4] cdcgov,work [4] help,suffering [4] cdcgov,cdcdirector [3] genitalium,isn Top Word Pairs in Tweet in G9:
[3] #medtwitter,#idtwitter [2] spent,20 [2] 20,minutes [2] minutes,explaining [2] explaining,patients [2] patients,infection [2] infection,thanked [2] thanked,great [2] great,social [2] social,worker Top Replied-To in Entire Graph:
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Top Mentioned in Entire Graph:
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Top Tweeters in Entire Graph:
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