The graph represents a network of 2,958 Twitter users whose recent tweets contained "digitalhealth", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 1/12/2023 5:00:35 PM. The network was obtained from Twitter on Friday, 13 January 2023 at 23:06 UTC.
The tweets in the network were tweeted over the 2697-day, 14-hour, 44-minute period from Tuesday, 25 August 2015 at 10:15 UTC to Friday, 13 January 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
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 : 2958
Unique Edges : 3272
Edges With Duplicates : 11145
Total Edges : 14417
Number of Edge Types : 9
Retweet : 2853
MentionsInRetweet : 4265
Mentions : 3498
Tweet : 1268
MentionsInReplyTo : 1765
MentionsInQuote : 435
Replies to : 201
Quote : 129
MentionsInQuoteReply : 3
Self-Loops : 2520
Reciprocated Vertex Pair Ratio : 0.0592025775271849
Reciprocated Edge Ratio : 0.111787072243346
Connected Components : 227
Single-Vertex Connected Components : 113
Maximum Vertices in a Connected Component : 2458
Maximum Edges in a Connected Component : 13642
Maximum Geodesic Distance (Diameter) : 12
Average Geodesic Distance : 3.585519
Graph Density : 0.00060136237159027
Modularity : 0.30098
NodeXL Version : 1.0.1.508
Data Import : The graph represents a network of 2,958 Twitter users whose recent tweets contained "digitalhealth", or who were replied to, mentioned, retweeted or quoted in those tweets, taken from a data set limited to a maximum of 5,000 tweets, tweeted between 3/26/2006 12:00:00 AM and 1/12/2023 5:00:35 PM. The network was obtained from Twitter on Friday, 13 January 2023 at 23:06 UTC.
The tweets in the network were tweeted over the 2697-day, 14-hour, 44-minute period from Tuesday, 25 August 2015 at 10:15 UTC to Friday, 13 January 2023 at 01:00 UTC.
There is an edge for each "replies-to" relationship in a tweet, an edge for each "mentions" relationship in a tweet, an edge for each "retweet" relationship in a tweet, an edge for each "quote" relationship in a tweet, an edge for each "mention in retweet" relationship in a tweet, an edge for each "mention in reply-to" relationship in a tweet, an edge for each "mention in quote" relationship in a tweet, an edge for each "mention in quote reply-to" relationship in a tweet, and a self-loop edge for each tweet that is not from above.
Layout Algorithm : The graph was laid out using the Harel-Koren Fast Multiscale layout algorithm.
Graph Source : TwitterSearch2
Graph Term : digitalhealth
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 : Betweenness Centrality
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[829] #healthtech,#smartcity [828] #csuite,#digitalhealth [828] #datascientist,#csuite [828] #digitalhealth,#web3 [825] #web3,#cx [825] #cx,#ehealth [824] #ehealth,#finserv [670] #smartcity,#5g [669] #5g,#datascientist [460] chidambara09,#healthtech Top Word Pairs in Tweet in G1:
[698] #healthtech,#smartcity [697] #datascientist,#csuite [697] #digitalhealth,#web3 [697] #csuite,#digitalhealth [696] #ehealth,#finserv [696] #cx,#ehealth [696] #web3,#cx [587] #smartcity,#5g [586] #5g,#datascientist [364] #frenchtech,chidambara09 Top Word Pairs in Tweet in G2:
[73] #digitalhealth,#healthcare [73] #vive2023,#healthtech [71] #healthcare,#medtech [55] #healthtech,#digitalhealth [43] #ces2023,#vive2023 [38] theviveevent,ces [37] #medtech,theviveevent [37] ces,inverita_ [35] inverita_,#developmentpartner [29] #medtech,#developmentpartner Top Word Pairs in Tweet in G3:
[82] #web3,#metaverse [68] transaction,based [68] #blockchain,changing [68] based,#industries [68] changing,#future [68] #future,transaction [65] #fintech,#web3 [64] #industries,techmenttech [61] techmenttech,#fintech [57] khulood_almani,#blockchain Top Word Pairs in Tweet in G4:
[131] digital,health [104] #digitalhealth,#ehealth [94] #digital,#digitalhealth [84] #csuite,#digitalhealth [84] #healthtech,#smartcity [84] #digitalhealth,#web3 [84] #datascientist,#csuite [83] #ehealth,#finserv [83] #cx,#ehealth [83] #web3,#cx Top Word Pairs in Tweet in G5:
[21] #digital,#digitalhealth [18] daily,#digitalhealth [17] dm,#algorithms [17] recover,account [17] #cloud,#ai [17] #wearables,#health [17] hack,recover [17] #datascience,#python [17] #python,#cloud [17] #algorithms,#wearables Top Word Pairs in Tweet in G6:
[144] #digitalhealth,#socialmedia [97] #digitalhealth,#digital [85] #digital,#digitalhealth [70] #socialmedia,#digitalmarketing [56] #digital,#health [51] #digitalhealth,#health [50] #rstats,#rectajs [50] #iot,#nlp [50] #analytics,#5g [50] #iiot,#ml Top Word Pairs in Tweet in G7:
[13] digital,health [9] #jmir,#preprint [9] check,interesting [9] interesting,#jmir [8] digital,strategy [8] keeps,pace [8] baptisthealthjx,keeps [8] growth,digital [8] talks,baptisthealthjx [8] booming,growth Top Word Pairs in Tweet in G8:
[53] rule,rule [53] infarction,using [53] myocardial,infarction [53] optimising,early [53] using,biomarkers [53] early,rule [53] biomarkers,#meded [53] rule,myocardial [52] #meded,#medtwitte [52] apothera,optimising Top Word Pairs in Tweet in G9:
[17] full,story [8] nhs,digital [7] health,inequalities [7] apps',data [7] data,reduce [7] reduce,health [7] #inequalities,good [7] read,nhs [7] digital,apps' [7] reducing,#inequalities Top Word Pairs in Tweet in G10:
[39] #mhealth,interventions [36] limited,social [36] widen,existing [36] evidence,limited [36] social,inequality [36] inequality,indicators [36] reduce,widen [36] interventions,reduce [36] existing,inequalities [36] inequalities,evidence Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
Top Replied-To in G3:
Top Replied-To in G4:
Top Replied-To in G6:
Top Replied-To in G9:
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 Mentioned in G7:
Top Mentioned in G8:
Top Mentioned in G9:
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:
Top Tweeters in G6:
Top Tweeters in G7:
Top Tweeters in G8:
Top Tweeters in G9:
Top Tweeters in G10: