The graph represents a network of 4,532 Twitter users whose recent tweets contained "#ACSCC19", 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 Wednesday, 30 October 2019 at 22:34 UTC.
The tweets in the network were tweeted over the 8-day, 20-hour, 33-minute period from Tuesday, 22 October 2019 at 01:47 UTC to Wednesday, 30 October 2019 at 22:21 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 : 4532
Unique Edges : 18563
Edges With Duplicates : 24411
Total Edges : 42974
Self-Loops : 1119
Reciprocated Vertex Pair Ratio : 0.0960330113476508
Reciprocated Edge Ratio : 0.175237443313083
Connected Components : 87
Single-Vertex Connected Components : 68
Maximum Vertices in a Connected Component : 4402
Maximum Edges in a Connected Component : 42786
Maximum Geodesic Distance (Diameter) : 9
Average Geodesic Distance : 3.018272
Graph Density : 0.00113827992433414
Modularity : 0.30423
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 4,532 Twitter users whose recent tweets contained "#ACSCC19", 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 Wednesday, 30 October 2019 at 22:34 UTC.
The tweets in the network were tweeted over the 8-day, 20-hour, 33-minute period from Tuesday, 22 October 2019 at 01:47 UTC to Wednesday, 30 October 2019 at 22:21 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 : TwitterSearch
Graph Term : #ACSCC19
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
Vertex Alpha : Followers
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[975] #some4iqlatam,#some4surgery [974] amcollsurgeons,#acscc19 [902] #some4surgery,#some4surgicaleducation [851] #acscc19,#some4iqlatam [782] juliomayol,swexner [667] #some4surgicaleducation,pferrada1 [616] swexner,misirg1 [566] pferrada1,pipecabrerav [556] #aws2019,#acscc19 [552] pipecabrerav,juliomayol Top Word Pairs in Tweet in G1:
[459] #aws2019,#acscc19 [405] amcollsurgeons,#acscc19 [227] #acscc19,#aws2019 [171] #acscc19,amcollsurgeons [170] #acscc19,#awsatacs [146] clinical,congress [129] womensurgeons,#acscc19 [115] md,facs [97] scott,conner [94] don,t Top Word Pairs in Tweet in G2:
[961] #some4iqlatam,#some4surgery [890] #some4surgery,#some4surgicaleducation [821] #acscc19,#some4iqlatam [771] juliomayol,swexner [656] #some4surgicaleducation,pferrada1 [605] swexner,misirg1 [557] pferrada1,pipecabrerav [543] pipecabrerav,juliomayol [297] jjcolemanmd,salo75 [282] years,old Top Word Pairs in Tweet in G3:
[230] amcollsurgeons,#acscc19 [175] clinical,congress [114] #acscc19,amcollsurgeons [105] amcollsurgeons,clinical [94] global,surgery [84] san,francisco [74] american,college [66] congress,#acscc19 [61] college,surgeons [55] discrimination,abuse Top Word Pairs in Tweet in G4:
[382] national,survey [382] survey,7 [382] 7,409 [382] 409,general [382] general,surgery [382] surgery,residents [382] residents,32 [382] 32,reported [382] reported,gender [382] gender,discrimination Top Word Pairs in Tweet in G5:
[28] da,vinci [25] #surged,#meded [16] booth,425 [12] amcollsurgeons,#acscc19 [10] #meded,#acscc19 [10] find,out [10] vinci,xi [10] xi,versatile [10] versatile,flexible [10] flexible,boom Top Word Pairs in Tweet in G6:
[9] amcollsurgeons,#acscc19 [8] clinical,congress [7] #acscc19,#acscc2019 [7] #acscc2019,#acscc19 [6] trauma,care [5] series,finale [5] finale,behindtheknife [5] behindtheknife,astschimera [5] astschimera,#transplantsurgery [5] #transplantsurgery,grab Top Word Pairs in Tweet in G7:
[40] amcollsurgeons,#acscc19 [15] #acscc19,amcollsurgeons [14] md,facs [10] excellence,research [10] research,award [10] umiamimedicine,umiamimednews [9] churchill,lecture [8] check,out [7] schwaitzberg,chair [7] san,francisco Top Word Pairs in Tweet in G8:
[15] booth,330 [13] san,francisco [12] physician,mba [11] stop,booth [11] visit,booth [11] 330,learn [10] learn,more [8] mba,program [6] clinical,congress [6] kelley,physician Top Word Pairs in Tweet in G9:
[8] #sytyco19,#acscc19 [7] #acscc19,#aws2019 [7] skill,competition [6] stanfordsurgery,opnotes [6] academicsurgery,fall [6] lauren,mctaggart [6] representing,south [6] south,texas [6] rasacs,#sytyco19 [5] come,hear Top Word Pairs in Tweet in G10:
[5] alexandra,hernandez [5] hernandez,presenting [5] presenting,acs [5] acs,e [5] e,poster [5] poster,session [5] session,representing [5] representing,ohsu [5] ohsu,excellence [5] excellence,ohsusurgery Top Replied-To in Entire Graph:
Top Replied-To in G1:
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Top Replied-To in G6:
Top Replied-To in G7:
Top Replied-To in G9:
Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Mentioned in G6:
Top Mentioned in G7:
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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:
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Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: