The graph represents a network of 2,571 Twitter users whose tweets in the requested range contained "govtech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 07 December 2021 at 00:22 UTC.
The requested start date was Monday, 06 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 13-day, 23-hour, 26-minute period from Monday, 22 November 2021 at 01:08 UTC to Monday, 06 December 2021 at 00:35 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 : 2571
Unique Edges : 1942
Edges With Duplicates : 5998
Total Edges : 7940
Number of Edge Types : 5
Retweet : 2302
MentionsInRetweet : 3151
Replies to : 87
Mentions : 1222
Tweet : 1178
Self-Loops : 1245
Reciprocated Vertex Pair Ratio : 0.0485908649173955
Reciprocated Edge Ratio : 0.092678405931418
Connected Components : 517
Single-Vertex Connected Components : 314
Maximum Vertices in a Connected Component : 1186
Maximum Edges in a Connected Component : 5552
Maximum Geodesic Distance (Diameter) : 14
Average Geodesic Distance : 5.048083
Graph Density : 0.000489900067650704
Modularity : 0.387819
NodeXL Version : 1.0.1.447
Data Import : The graph represents a network of 2,571 Twitter users whose tweets in the requested range contained "govtech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 07 December 2021 at 00:22 UTC.
The requested start date was Monday, 06 December 2021 at 01:01 UTC and the maximum number of days (going backward) was 14.
The maximum number of tweets collected was 7,500.
The tweets in the network were tweeted over the 13-day, 23-hour, 26-minute period from Monday, 22 November 2021 at 01:08 UTC to Monday, 06 December 2021 at 00:35 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 : govtech
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:
[403] #aml,#crypto [333] #csr,#corpgov [322] #insurtech,#fintech [301] #corpgov,#aml [301] #crypto,#finserv [301] #finserv,#wef [301] #wef,#datagovernance [296] #datagovernance,#futureofwork [292] #futureofwork,#insurtech [290] #fintech,#regtech Top Word Pairs in Tweet in G1:
[378] #aml,#crypto [308] #csr,#corpgov [297] #insurtech,#fintech [277] #corpgov,#aml [277] #crypto,#finserv [277] #finserv,#wef [277] #wef,#datagovernance [272] #datagovernance,#futureofwork [269] #futureofwork,#insurtech [267] #fintech,#regtech Top Word Pairs in Tweet in G2:
[12] federal,funding [12] high,speed [11] speed,internet [10] learn,more [10] digital,transformation [10] funding,broadband [10] broadband,brings [10] brings,hope [10] hope,connecting [10] connecting,americans Top Word Pairs in Tweet in G3:
[23] #csr,#corpgov [23] #aml,#crypto [22] martinhoyes,#csr [22] #corpgov,#aml [22] #crypto,#finserv [22] #finserv,#wef [22] #wef,#datagovernance [22] #datagovernance,#futureofwork [22] #insurtech,#fintech [21] #futureofwork,#insurtech Top Word Pairs in Tweet in G4:
[27] #fintech,#finserv [22] #finserv,#insurtech [21] #insurtech,#proptech [21] #proptech,#regtech [17] enricomolinari,#govtech [16] uk,ready [16] ready,future [16] future,digital [16] digital,id [16] #govtech,#regtech Top Word Pairs in Tweet in G5:
[240] elko,county [120] county,nev [120] nev,change [120] change,voting [120] voting,tech [120] tech,dominion [120] dominion,public [120] public,trust [120] trust,guess [120] guess,elko Top Word Pairs in Tweet in G6:
[71] facing,difficulties [71] difficulties,setting [71] setting,burp_suite [71] burp_suite,android [71] android,device [71] device,check [71] check,proxy [71] proxy,agent [71] agent,tool [71] tool,automate Top Word Pairs in Tweet in G7:
[67] #govtech,ukauthority [21] #procurement,#govtech [12] govexec,nextgov [11] #govtech,nextgov [11] #govtech,teams [9] #govtech,#emergingtech [8] teams,know [8] know,speed [8] speed,delivery [8] delivery,application Top Word Pairs in Tweet in G8:
[40] governo,digital [39] embarque,seguro [39] solução,governo [38] você,viu [38] viu,embarque [38] seguro,solução [38] digital,facilitando [38] facilitando,viagens [38] viagens,cidadão [38] cidadão,embarcamos Top Word Pairs in Tweet in G9:
[9] citizenlab,looking [9] govtech,remote [9] remote,#react [8] help,hurt [8] hurt,cyber [8] cyber,defense [8] #decipher,algorand [8] algorand,first [7] tomorrow,#decipher [7] first,person Top Word Pairs in Tweet in G10:
[21] artificial,intelligence [19] #iaem,#nema [17] #disasters,#disasterzone [13] intelligence,help [13] government,technology [12] help,hurt [12] hurt,cyber [12] cyber,defense [12] cybersecurity,stakes [12] stakes,higher Top Replied-To in Entire Graph:
Top Replied-To in G1:
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Top Replied-To in G7:
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Top Replied-To in G9:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Mentioned in G10:
Top Tweeters in Entire Graph:
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Top Tweeters in G10: