The graph represents a network of 664 Twitter users whose tweets in the requested range contained "traveltech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 18 November 2021 at 02:53 UTC.
The requested start date was Thursday, 18 November 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, 20-hour, 32-minute period from Thursday, 04 November 2021 at 01:15 UTC to Wednesday, 17 November 2021 at 21:47 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 : 664
Unique Edges : 722
Edges With Duplicates : 2216
Total Edges : 2938
Number of Edge Types : 5
Retweet : 808
MentionsInRetweet : 1377
Mentions : 505
Replies to : 14
Tweet : 234
Self-Loops : 299
Reciprocated Vertex Pair Ratio : 0.0856299212598425
Reciprocated Edge Ratio : 0.157751586582049
Connected Components : 134
Single-Vertex Connected Components : 91
Maximum Vertices in a Connected Component : 409
Maximum Edges in a Connected Component : 2540
Maximum Geodesic Distance (Diameter) : 9
Average Geodesic Distance : 3.668097
Graph Density : 0.00250549710152829
Modularity : 0.319525
NodeXL Version : 1.0.1.447
Data Import : The graph represents a network of 664 Twitter users whose tweets in the requested range contained "traveltech", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Thursday, 18 November 2021 at 02:53 UTC.
The requested start date was Thursday, 18 November 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, 20-hour, 32-minute period from Thursday, 04 November 2021 at 01:15 UTC to Wednesday, 17 November 2021 at 21:47 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 : traveltech
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:
[388] #usa,#tech [243] #fashion,enilev [243] #women,#usa [243] #fashiontech,#ehealth [238] #digital,#women [230] #tech,#fashiontech [228] enilev,#digital [227] #tvtime,#movies [208] #ehealth,#datascience [203] #datascience,#finserv Top Word Pairs in Tweet in G1:
[326] #usa,#tech [204] #fashion,enilev [204] #women,#usa [204] #fashiontech,#ehealth [199] #digital,#women [192] #tech,#fashiontech [190] #tvtime,#movies [189] enilev,#digital [183] #ehealth,#datascience [177] #datascience,#finserv Top Word Pairs in Tweet in G2:
[20] travel,apps [20] apps,know [20] know,comments [20] comments,others [20] others,appreciate [20] appreciate,recommendation [20] recommendation,well [20] well,#traveltech [20] #traveltech,#travelapps [20] #travelapps,#traveler Top Word Pairs in Tweet in G3:
[53] #usa,#tech [33] #fashion,enilev [33] enilev,#digital [33] #digital,#women [33] #women,#usa [33] #fashiontech,#ehealth [32] #tech,#fashiontech [31] #tvtime,#movies [28] chidambara09,#tvtime [22] #ehealth,#datascience Top Word Pairs in Tweet in G4:
[14] #traveltech,#trendbook [14] #trendbook,welcomecitylab [11] tourism,innovation [11] innovation,summit [9] experiencias,inmersivas [9] inmersivas,caza [9] caza,turismo [9] turismo,inclusivo [9] inclusivo,#traveltech [9] #traveltech,respuesta Top Word Pairs in Tweet in G5:
[26] #decesos,#traveltech [25] #insurchallenges,#decesos [24] pierdas,evento [24] #traveltech,edición [15] evento,gratuito [15] gratuito,#insurchallenges [12] santalucía,impulsa [11] día,18 [11] inn_somnia,santalucia_imp [10] santalucia_seg,pierdas Top Word Pairs in Tweet in G6:
[15] #travel,#traveltech [7] #traveltech,#aviation [7] wonder,luggage [7] luggage,gets [7] gets,packed [7] packed,plane [7] plane,#travel [7] #traveltech,nowthisnews [6] augmented,reality [6] reality,airplane Top Word Pairs in Tweet in G7:
[6] more,#traveltech [5] ve,matched [5] matched,hello [5] hello,json [5] json,api [5] api,dreams [5] dreams,find [5] find,more [4] look,far [4] far,we've Top Word Pairs in Tweet in G8:
[8] john,prince [7] eddytravels,#ai [6] #ai,assistant [5] eddytravels,discover [5] eddytravels,finals [5] finals,150 [5] 150,travel [5] travel,startups [5] startups,futuretravelco [5] futuretravelco,summit Top Word Pairs in Tweet in G9:
[18] shape,future [15] hybrid,event [14] future,#traveltech [14] 30,nov [12] #traveltech,partnering [12] partnering,traveltechscot [12] traveltechscot,deliver [12] deliver,hybrid [12] event,30 [11] uoe_efi,shape Top Word Pairs in Tweet in G10:
[9] sportihome,lève [7] montpelliéraine,sportihome [7] lève,millions [7] millions,euros [5] #traveltech,montpelliéraine [5] euros,auprès [5] auprès,fonds [5] fonds,luxembourgeois [5] luxembourgeois,#accurafy4 [4] latribunemtp,#traveltech Top Replied-To in Entire Graph:
Top Replied-To in G1:
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Top Replied-To in G5:
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Top Mentioned in Entire Graph:
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Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G10: