The graph represents a network of 1,942 Twitter users whose recent tweets contained "#ILC2019", 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 Sunday, 14 April 2019 at 12:59 UTC.
The tweets in the network were tweeted over the 9-day, 13-hour, 23-minute period from Thursday, 04 April 2019 at 23:26 UTC to Sunday, 14 April 2019 at 12:49 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 : 1942
Unique Edges : 7342
Edges With Duplicates : 12970
Total Edges : 20312
Self-Loops : 1114
Reciprocated Vertex Pair Ratio : 0.105013013466108
Reciprocated Edge Ratio : 0.190066564260113
Connected Components : 62
Single-Vertex Connected Components : 42
Maximum Vertices in a Connected Component : 1807
Maximum Edges in a Connected Component : 20110
Maximum Geodesic Distance (Diameter) : 7
Average Geodesic Distance : 2.957469
Graph Density : 0.00259058285328626
Modularity : 0.259146
NodeXL Version : 1.0.1.410
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[546] #ilc2019,easledu [436] easlnews,easledu [393] easledu,easlnews [358] #ilc2019,easlnews [313] easlnews,#ilc2019 [299] liver,disease [280] #ilc2019,#nohep [243] #nafld,#nash [215] viral,hepatitis [207] easledu,#ilc2019 Top Word Pairs in Tweet in G1:
[254] #ilc2019,#nohep [188] #nohep,#ilc2019 [155] #ilc2019,easledu [148] #ilc2019,easlnews [145] viral,hepatitis [134] #elpasymposium2019,#ilc2019 [119] easlnews,easledu [114] hepatitis,b [114] easlnews,#ilc2019 [111] #ilc2019,#easl2019 Top Word Pairs in Tweet in G2:
[263] #ilc2019,easledu [190] easlnews,easledu [152] #ilc2019,easlnews [129] easlnews,#ilc2019 [100] liver,disease [95] easledu,easlnews [84] easledu,#ilc2019 [63] #ilc2019,pgc [48] #easl2019,#ilc2019 [46] session,#ilc2019 Top Word Pairs in Tweet in G3:
[179] easledu,easlnews [112] #nafld,#nash [110] #ilc2019,easledu [106] easlnews,easledu [70] easledu,#ilc2019 [70] phase,3 [69] #nash,#pbc [68] #ilc2019,today [68] during,#ilc2019 [67] booth,#240 Top Word Pairs in Tweet in G4:
[19] #ilc2019,#mdedgetweets [13] people,inject [13] inject,drugs [12] up,43 [12] 43,world's [12] world's,future [12] future,#hepatitisc [12] #hepatitisc,infections [12] infections,prevented [12] prevented,transmission Top Word Pairs in Tweet in G5:
[44] one,death [38] #ilc2019,#easl2019 [30] viral,hepatitis [23] scale,up [22] real,world [22] world,data [22] data,viral [22] hepatitis,session [22] session,#ilc2019 [22] #easl2019,one Top Word Pairs in Tweet in G6:
[31] liver,disease [22] advanced,fibrosis [20] fibrosis,due [20] #nash,chronic [20] chronic,progressive [20] progressive,liver [20] disease,lead [20] lead,fibrosis [20] fibrosis,impair [20] impair,liver Top Word Pairs in Tweet in G7:
[6] international,liver [6] liver,congress [5] liver,disease [4] april,2019 [4] vienna,austria [4] fatty,liver [3] messe,wien [3] wien,exhibition [3] exhibition,congress [3] congress,center Top Word Pairs in Tweet in G8:
[23] easlnews,#ilc2019 [18] excess,consumption [18] consumption,sugar [18] sugar,soft [18] soft,drinks [18] drinks,high [18] high,fructose [18] fructose,corn [18] corn,syrup [18] syrup,impact Top Word Pairs in Tweet in G9:
[10] 3,cups [10] cups,coffee [10] coffee,day [10] day,reduces [10] reduces,risk [10] risk,liver [10] liver,fibrosis [10] fibrosis,memorable [10] memorable,take [10] take,home Top Word Pairs in Tweet in G10:
[17] manisha,balwani [16] #porphyria,isolating [16] isolating,disease [16] disease,even [16] even,w [16] w,caring [16] caring,physicians [16] physicians,loved [16] loved,ones [16] ones,patient 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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Top Tweeters in G10: