The graph represents a network of 623 Twitter users whose recent tweets contained "tapairportugal", 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 Friday, 10 January 2020 at 00:46 UTC.
The tweets in the network were tweeted over the 7-day, 20-hour, 40-minute period from Thursday, 02 January 2020 at 03:26 UTC to Friday, 10 January 2020 at 00:06 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 : 623
Unique Edges : 811
Edges With Duplicates : 946
Total Edges : 1757
Number of Edge Types : 5
Replies to : 897
Retweet : 33
Mentions : 647
MentionsInRetweet : 140
Tweet : 40
Self-Loops : 42
Reciprocated Vertex Pair Ratio : 0.252293577981651
Reciprocated Edge Ratio : 0.402930402930403
Connected Components : 12
Single-Vertex Connected Components : 9
Maximum Vertices in a Connected Component : 610
Maximum Edges in a Connected Component : 1745
Maximum Geodesic Distance (Diameter) : 6
Average Geodesic Distance : 2.436218
Graph Density : 0.00281802088225731
Modularity : 0.337331
NodeXL Version : 1.0.1.422
Data Import : The graph represents a network of 623 Twitter users whose recent tweets contained "tapairportugal", 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 Friday, 10 January 2020 at 00:46 UTC.
The tweets in the network were tweeted over the 7-day, 20-hour, 40-minute period from Thursday, 02 January 2020 at 03:26 UTC to Friday, 10 January 2020 at 00:06 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 : TwitterSearch
Graph Term : tapairportugal
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 : In-Degree
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[50] private,message [36] mensagem,privada [28] please,know [28] possible,thank [28] customer,service [26] #tapairportugal,#tapdiscount [23] message,thank [21] tap,air [21] air,portugal [20] ticket,number Top Word Pairs in Tweet in G1:
[50] private,message [35] mensagem,privada [28] possible,thank [28] please,know [23] #tapairportugal,#tapdiscount [23] message,thank [20] booking,reference [20] ticket,number [19] order,verify [18] código,reserva Top Word Pairs in Tweet in G2:
[8] worst,airline [8] customer,service [8] cancelled,flight [7] still,waiting [7] visitportugal,tapairportugal [6] trekbikes,therealberghaus [6] therealberghaus,polarglobal [6] polarglobal,tapairportugal [6] tapairportugal,portuguesemrs [6] portuguesemrs,whereistarablog Top Word Pairs in Tweet in G3:
[9] vkalavri,tapairportugal [6] tapairportugal,alitalia [4] without,luggage [4] tapairportugal,aircanada [3] julinha_mello15,tapairportugal [3] tapairportugal,flight [2] day,without [2] petite_lucia,tapairportugal [2] najessica_,julinha_mello15 [2] alitalia,breve Top Word Pairs in Tweet in G4:
[2] milhasedestinos,pontoseviagens [2] pontoseviagens,smilesoficial [2] smilesoficial,lifemiles [2] lifemiles,british_airways [2] british_airways,thaiairways [2] thaiairways,koreanair_ke [2] koreanair_ke,cathaypacific Top Word Pairs in Tweet in G5:
[12] tapairportugal,tapbrasil [2] customer,service [2] phone,number [2] calling,usa [2] tapbrasil,vcs [2] kd,meu [2] flight,sao [2] sao,paulo [2] paulo,dublin [2] dublin,already Top Word Pairs in Tweet in G6:
[2] staralliance,tapairportugal [2] crissi_beth,laurettamylove [2] laurettamylove,visitportugal [2] visitportugal,portugal [2] portugal,tapairportugal [2] tapairportugal,usembportugal [2] usembportugal,golisbon [2] golisbon,turismodelisboa [2] turismodelisboa,startuplisboa [2] startuplisboa,visiteurope Top Word Pairs in Tweet in G7:
[3] one,busiest [3] busiest,days [3] days,madeiraairports [3] madeiraairports,#aviation [3] #aviation,#planes [3] #planes,#planespotting [3] #planespotting,#avgeek [3] #avgeek,#madeira [3] #madeira,#landing [3] #landing,#airport Top Word Pairs in Tweet in G8:
[7] airline_ratings,tap [6] seguramente,uma [6] uma,melhores [6] melhores,notícias [6] notícias,podíamos [6] podíamos,receber [6] receber,início [6] início,2020 [6] 2020,acordo [6] acordo,site Top Word Pairs in Tweet in G9:
[9] mvanhulten,tapairportugal [3] take,another [3] another,plane [2] always,late 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 G5:
Top Replied-To in G6:
Top Replied-To in G7:
Top Replied-To in G8:
Top Replied-To in G9:
Top Replied-To in G10:
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: