The graph represents a network of 79 Twitter users whose recent tweets contained "#EPHFellow", 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 08:36 UTC.
The tweets in the network were tweeted over the 5-day, 4-hour, 35-minute period from Tuesday, 19 November 2019 at 16:45 UTC to Sunday, 24 November 2019 at 21:20 UTC.
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 : 79
Unique Edges : 215
Edges With Duplicates : 176
Total Edges : 391
Self-Loops : 45
Reciprocated Vertex Pair Ratio : 0.183856502242152
Reciprocated Edge Ratio : 0.310606060606061
Connected Components : 1
Single-Vertex Connected Components : 0
Maximum Vertices in a Connected Component : 79
Maximum Edges in a Connected Component : 391
Maximum Geodesic Distance (Diameter) : 4
Average Geodesic Distance : 2.261176
Graph Density : 0.0428432327166504
Modularity : 0.294862
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 79 Twitter users whose recent tweets contained "#EPHFellow", 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 08:36 UTC.
The tweets in the network were tweeted over the 5-day, 4-hour, 35-minute period from Tuesday, 19 November 2019 at 16:45 UTC to Sunday, 24 November 2019 at 21:20 UTC.
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 : #EPHFellow
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:
[78] #eph2019,#ephfellow [39] public,health [28] #ephfellow,#eph2019 [22] #eph2019,plenary [21] digital,health [19] health,services [17] listen,dr [17] dr,bernardette [17] bernardette,kumar [17] kumar,debunk Top Word Pairs in Tweet in G1:
[22] #eph2019,#ephfellow [14] #eph2019,plenary [14] #dph2019,#eph2019 [12] listen,dr [12] dr,bernardette [12] bernardette,kumar [12] kumar,debunk [12] debunk,common [12] common,myths [12] myths,associated Top Word Pairs in Tweet in G2:
[13] #eph2019,#ephfellow [12] #ephfellow,#eph2019 [8] #eph2019,#publichealth [6] public,health [6] john,middleton [6] middleton,aspheroffice [6] aspheroffice,policy [6] policy,wrong [6] wrong,money [6] money,world Top Word Pairs in Tweet in G3:
[27] #eph2019,#ephfellow [9] #ephfellow,#eph2019 [5] health,services [4] #healthcare,access [4] #ephfellow,rki_de [4] #migranthealth,ephconference [3] #migranthealth,#eph2019 [3] maryam,gardisi [3] #migranthealth,monitoring [3] europe,ephconference Top Word Pairs in Tweet in G4:
[14] public,health [11] digital,health [9] #eph2019,#ephfellow [6] interested,digital [6] health,check [6] check,out [6] out,paper [6] paper,prepared [6] prepared,stefanbuttigieg [6] stefanbuttigieg,odoneanna Top Word Pairs in Tweet in G5:
[7] public,health [6] ephconference,euphacts [6] #dph2019,#ephfellow [5] #eph2019,nodexl [5] nodexl,ephconference [5] euphacts,sfspasso [5] sfspasso,euphanxt [5] euphanxt,ucl_dphe [5] ucl_dphe,natasha_azzmus [5] natasha_azzmus,eurohealthnet Top Word Pairs in Tweet in G6:
[5] #eph2019,#ephfellow [4] #euphanxt,#ephfellow [2] always,bears [2] bears,repeating [2] repeating,#migration [2] #migration,background [2] background,itself [2] itself,risk [2] risk,factor [2] factor,experiences Top Replied-To in Entire Graph:
Top Replied-To in G2:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
Top Mentioned in G2:
Top Mentioned in G3:
Top Mentioned in G4:
Top Mentioned in G5:
Top Mentioned in G6:
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: