The graph represents a network of 1,412 Twitter users whose recent tweets contained "#EPH2019", 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 Monday, 25 November 2019 at 08:10 UTC.
The tweets in the network were tweeted over the 9-day, 10-hour, 48-minute period from Friday, 15 November 2019 at 21:15 UTC to Monday, 25 November 2019 at 08:04 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 : 1412
Unique Edges : 5448
Edges With Duplicates : 7475
Total Edges : 12923
Self-Loops : 612
Reciprocated Vertex Pair Ratio : 0.118155175073269
Reciprocated Edge Ratio : 0.211339495102773
Connected Components : 30
Single-Vertex Connected Components : 21
Maximum Vertices in a Connected Component : 1370
Maximum Edges in a Connected Component : 12872
Maximum Geodesic Distance (Diameter) : 8
Average Geodesic Distance : 3.002751
Graph Density : 0.00363844981659683
Modularity : 0.286202
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 1,412 Twitter users whose recent tweets contained "#EPH2019", 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 Monday, 25 November 2019 at 08:10 UTC.
The tweets in the network were tweeted over the 9-day, 10-hour, 48-minute period from Friday, 15 November 2019 at 21:15 UTC to Monday, 25 November 2019 at 08:04 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".
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch
Graph Term : #EPH2019
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:
[871] public,health [275] ephconference,#eph2019 [252] #eph2019,#euphaateph2019 [213] #eph2019,ephconference [164] #eph2019,euphacts [145] #euphaateph2019,#eph2019 [123] digital,health [120] looking,forward [117] health,inequalities [109] santé,publique Top Word Pairs in Tweet in G1:
[382] public,health [141] #eph2019,#euphaateph2019 [88] ephconference,#eph2019 [73] looking,forward [69] #eph2019,euphacts [69] #euphaateph2019,#eph2019 [61] digital,health [57] 11,10 [53] health,systems [50] #eph2019,ephconference Top Word Pairs in Tweet in G2:
[143] public,health [92] ephconference,#eph2019 [69] #dph2019,#eph2019 [59] #eph2019,ephconference [46] #eph2019,#ephfellow [36] check,out [35] health,inequalities [32] health,services [29] #eph2019,#dph2019 [28] #eph2019,plenary Top Word Pairs in Tweet in G3:
[106] public,health [92] santé,publique [35] européenne,santé [33] maison,france [29] conférence,européenne [27] #eph2019,ephconference [25] à,marseille [24] euphacts,sfspasso [23] ephconference,euphacts [21] publique,#eph2019 Top Word Pairs in Tweet in G4:
[148] public,health [72] #eph2019,euphacts [70] #eph2019,#euphaateph2019 [42] #euphaateph2019,#eph2019 [36] life,expectancy [36] ihme_uw,#eph2019 [28] digital,health [28] ephconference,#eph2019 [24] social,care [24] new,#gbdstudy Top Word Pairs in Tweet in G5:
[50] #eph2019,ephconference [39] public,health [36] health,inequalities [23] ephconference,#eph2019 [22] lucinda,hiam [18] infant,mortality [17] #eph2019,plenary [17] deaths,despair [12] ageing,crisis [12] johan,mackenbach Top Word Pairs in Tweet in G6:
[17] #eph2019,phmrsection [17] health,information [16] pre,conference [14] environmental,#burdenofdisease [13] public,health [10] learn,more [10] #eph2019,#burdeneu [10] don,t [9] rki_de,marikentijhuis [9] #eph2019,#euphaateph2019 Top Word Pairs in Tweet in G7:
[19] social,biological [17] workshop,social [17] processes,over [17] over,lifecourse [15] biological,processes [15] based,lifepath_eu [14] lifepath_eu,social2bio [12] social,inequalities [10] lifecourse,social [10] 14,50 Top Word Pairs in Tweet in G8:
[4] public,health [3] pre,conference [2] climate,change [2] very,interesting [2] #eph2019,conference [2] out,#eph2019 [2] #eph2019,plenary Top Word Pairs in Tweet in G9:
[4] #telemedicine,#ehealth [4] #ehealth,une [4] une,étude [4] étude,menée [4] menée,france [4] france,l [4] l,dernier [4] dernier,montre [4] montre,seulement [4] seulement,14 Top Word Pairs in Tweet in G10:
[10] ephconference,#eph2019 [9] capacity,building [6] big,similarities [6] similarities,between [6] between,scottish [6] scottish,health [6] health,social [6] social,care [6] care,integration [6] integration,approach Top Replied-To in Entire Graph:
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Top Replied-To in G9:
Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G3:
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Top Tweeters in G10: