The graph represents a network of 1,882 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 April 2022 at 12:09 UTC.
The requested start date was Friday, 01 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500.
The tweets in the network were tweeted over the 2-day, 8-hour, 15-minute period from Tuesday, 29 March 2022 at 15:45 UTC to Friday, 01 April 2022 at 00:00 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 : 1882
Unique Edges : 2991
Edges With Duplicates : 17105
Total Edges : 20096
Number of Edge Types : 5
Mentions : 3074
Tweet : 788
Retweet : 6195
MentionsInRetweet : 9866
Replies to : 173
Self-Loops : 1207
Reciprocated Vertex Pair Ratio : 0.0542338709677419
Reciprocated Edge Ratio : 0.102887741441958
Connected Components : 228
Single-Vertex Connected Components : 149
Maximum Vertices in a Connected Component : 1513
Maximum Edges in a Connected Component : 19635
Maximum Geodesic Distance (Diameter) : 10
Average Geodesic Distance : 3.380778
Graph Density : 0.00147710111913926
Modularity : 0.21253
NodeXL Version : 1.0.1.449
Data Import : The graph represents a network of 1,882 Twitter users whose tweets in the requested range contained "InsurTech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 01 April 2022 at 12:09 UTC.
The requested start date was Friday, 01 April 2022 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500.
The tweets in the network were tweeted over the 2-day, 8-hour, 15-minute period from Tuesday, 29 March 2022 at 15:45 UTC to Friday, 01 April 2022 at 00:00 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 : GraphServerTwitterSearch
Graph Term : InsurTech
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:
[3296] #iot,#iiot [2950] #javascript,#reactjs [2686] #reactjs,#wordpress [2355] #wordpress,#security [2165] #pytorch,#javascript [2113] #iiot,#pytorch [2095] #security,#defi [1996] #python,#iot [1627] #ai,#iot [1483] #python,#ai Top Word Pairs in Tweet in G1:
[199] #fintech,#insurtech [162] #fintech,#finserv [144] #finserv,#insurtech [139] #women,#ehealth [139] #ehealth,#ces2023 [139] #ces2023,#finserv [137] #cx,#women [125] #insurtech,#fintech [119] #healthtech,#datascientist [115] chidambara09,#healthtech Top Word Pairs in Tweet in G2:
[522] #women,#ehealth [512] #cx,#women [510] #ehealth,#ces2023 [487] #ces2023,#finserv [466] #healthtech,#datascientist [443] #usa,#cx [428] #digitalhealth,#cloud [410] #cloud,#usa [289] #datascientist,#digitalhealth [241] #100daysofcode,#javascript Top Word Pairs in Tweet in G3:
[158] #aiethics,#machinelearning [140] #machinelearning,#ai [114] #ai,#python [71] #python,#deeplearning [71] #healthtech,#datascientist [71] #women,#ehealth [71] #ehealth,#ces2023 [71] #ces2023,#finserv [70] chidambara09,#healthtech [69] #usa,#cx Top Word Pairs in Tweet in G4:
[3272] #iot,#iiot [2927] #javascript,#reactjs [2664] #reactjs,#wordpress [2337] #wordpress,#security [2147] #pytorch,#javascript [2094] #iiot,#pytorch [2075] #security,#defi [1977] #python,#iot [1580] #ai,#iot [1479] #python,#ai Top Word Pairs in Tweet in G5:
[9] insurtech,company [8] #techblogs,#community [7] #innovation,#insurtech [7] #fintech,#insurtech [7] atlanta,based [7] based,insurtech [7] company,double [7] double,staff [7] staff,10m [7] 10m,raise Top Word Pairs in Tweet in G6:
[3] inspiredbylaban,readingisourpas [3] readingisourpas,jgmacleodauthor [3] jgmacleodauthor,plstuartwrites [3] plstuartwrites,trilllindsay [3] trilllindsay,trhamby1 [3] trhamby1,tonyawrites [3] tonyawrites,tristanbtaylor [2] overcome,threat [2] threat,complacency [2] complacency,#cybersecurity Top Word Pairs in Tweet in G8:
[6] join,#insurance [5] free,#insurtech [5] #insurtech,#webinar [5] #webinar,info [5] info,#insurance [5] #insurtech,#sviaevents [4] future,insurance [3] sviaccelerator,join [3] #insurance,#digitaldistribution [3] #digitalagents,#digitalbrokers Top Word Pairs in Tweet in G9:
[2] techcrunch,mentions [2] mentions,insuremyteam [2] insuremyteam,yc [2] yc,w22 [2] w22,latest [2] latest,article [2] article,ycombinator [2] ycombinator,demo [2] demo,day [2] day,founders Top Word Pairs in Tweet in G10:
[10] innovate,find [10] jiaqi,anne [10] anne,nicolas [10] nicolas,secil [9] secil,axa_partners [7] #axa,innovate [7] find,jiaqi [7] axa_partners,axaventures [7] axaventures,axanext [6] axa,#axa Top Replied-To in Entire Graph:
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Top Replied-To in G9:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Tweeters in Entire Graph:
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Top Tweeters in G10: