The graph represents a network of 4,721 Twitter users whose tweets in the requested range contained "ecommerce", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 09 November 2019 at 08:44 UTC.
The requested start date was Wednesday, 06 November 2019 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000.
The tweets in the network were tweeted over the 1-day, 5-hour, 48-minute period from Monday, 04 November 2019 at 14:14 UTC to Tuesday, 05 November 2019 at 20:03 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 : 4721
Unique Edges : 4666
Edges With Duplicates : 1977
Total Edges : 6643
Number of Edge Types : 3
Mentions : 3555
Tweet : 2918
Replies to : 170
Self-Loops : 2918
Reciprocated Vertex Pair Ratio : 0.040650406504065
Reciprocated Edge Ratio : 0.078125
Connected Components : 2122
Single-Vertex Connected Components : 1384
Maximum Vertices in a Connected Component : 636
Maximum Edges in a Connected Component : 1301
Maximum Geodesic Distance (Diameter) : 18
Average Geodesic Distance : 7.642105
Graph Density : 0.000149350719288861
Modularity : 0.611102
NodeXL Version : 1.0.1.421
Data Import : The graph represents a network of 4,721 Twitter users whose tweets in the requested range contained "ecommerce", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 09 November 2019 at 08:44 UTC.
The requested start date was Wednesday, 06 November 2019 at 01:01 UTC and the maximum number of tweets (going backward in time) was 5,000.
The tweets in the network were tweeted over the 1-day, 5-hour, 48-minute period from Monday, 04 November 2019 at 14:14 UTC to Tuesday, 05 November 2019 at 20:03 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 : ecommerce
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 : Followers
Top Domains
Top Word Pairs in Tweet in Entire Graph:
[611] ð,ð [293] à,à [246] e,commerce [230] ðÿ,ðÿ [204] #sale,#tips [204] #tips,#deals [204] #ecommerce,#christmas [166] check,out [160] gt,gt [121] #fashion,#sale Top Word Pairs in Tweet in G1:
[131] ð,ð [95] e,commerce [90] #business,#ecommerce [84] #sale,#tips [84] #tips,#deals [84] #ecommerce,#christmas [83] #shopping,#shop [83] #shop,#sale [83] #deals,#business [75] check,out Top Word Pairs in Tweet in G2:
[99] gt,gt [20] mikequindazzi,gt [17] gt,#ai [15] sale,#brand [15] #brand,#tech [15] #tech,#technology [15] #technology,#innovation [15] #innovation,#bitcoin [15] #bitcoin,#bitcoins [15] #bitcoins,#salem Top Word Pairs in Tweet in G3:
[13] âž,ï [9] 2,shopify [8] e,commerce [7] ðÿ,ðÿ [7] shopify,email [5] big,ecommerce [5] ecommerce,news [5] news,week [5] week,1 [5] 1,alipay Top Word Pairs in Tweet in G4:
[22] cã,mo [17] mã,s [12] ðÿ,ðÿ [9] tu,ecommerce [8] vender,mã [8] curso,gratuito [6] mo,vender [6] amigos,â [6] â,hoy [6] hoy,abrimos Top Word Pairs in Tweet in G5:
[26] fetchy,delivers [26] service,solution [26] solution,#ecommerce [14] delivers,logistics [14] logistics,service [14] #ecommerce,#businesses [13] techcompanynews,fetchy [12] delivers,#logistics [12] #logistics,service [12] #ecommerce,businesses Top Word Pairs in Tweet in G6:
[11] holiday,season [9] dm,free [9] free,mock [9] mock,up [7] rush,#qeretail [6] check,out [6] up,risk [6] ns8inc,viabill_hq [6] viabill_hq,refersion [5] #ecommerce,#smallbiz Top Word Pairs in Tweet in G7:
[45] listen,now [20] check,out [14] out,more [13] top,8 [13] 8,threats [13] threats,against [13] against,#ecommerce [13] #ecommerce,#infographic [13] #infographic,check [13] more,handy Top Word Pairs in Tweet in G8:
[7] #ai,help [6] andrejordao,showcasing [6] showcasing,startup [6] startup,barkyndogs [6] barkyndogs,sophisticated [6] sophisticated,#ai [6] help,server [6] server,pets [6] pets,better [6] better,merge Top Word Pairs in Tweet in G9:
[24] monetizaciã,n [19] congreso,online [19] n,web [14] #ecommerce,monetizaciã [14] sobre,#ecommerce [12] online,gratuito [10] online,sobre [10] sobre,ecommerce [9] gratuito,sobre [8] ecommerce,monetizaciã Top Word Pairs in Tweet in G10:
[10] e,commerce [6] #retail,#ecommerce [5] ricarda,raemy [5] raemy,raemyricarda [4] 2019,ricarda [4] swisspost,#ecommerce [3] tze,sind [3] sind,gescheitert [3] â,quã [3] quã,abandonamos Top Replied-To in Entire Graph:
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Top Replied-To in G10:
Top Mentioned in Entire Graph:
Top Mentioned in G1:
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Top Tweeters in Entire Graph:
Top Tweeters in G1:
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Top Tweeters in G9:
Top Tweeters in G10: