The graph represents a network of 6,289 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 07 November 2020 at 01:46 UTC.
The requested start date was Friday, 06 November 2020 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 4-day, 9-hour, 59-minute period from Sunday, 01 November 2020 at 15:00 UTC to Friday, 06 November 2020 at 01:00 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 : 6289
Unique Edges : 7433
Edges With Duplicates : 13141
Total Edges : 20574
Number of Edge Types : 5
Tweet : 1665
MentionsInRetweet : 8150
Mentions : 5790
Retweet : 4557
Replies to : 412
Self-Loops : 1825
Reciprocated Vertex Pair Ratio : 0.0362751400495913
Reciprocated Edge Ratio : 0.0700106345267636
Connected Components : 908
Single-Vertex Connected Components : 464
Maximum Vertices in a Connected Component : 3877
Maximum Edges in a Connected Component : 16470
Maximum Geodesic Distance (Diameter) : 17
Average Geodesic Distance : 5.256697
Graph Density : 0.000285344134534348
Modularity : 0.480516
NodeXL Version : 1.0.1.441
Data Import : The graph represents a network of 6,289 Twitter users whose tweets in the requested range contained "smartcity OR smartcities", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Saturday, 07 November 2020 at 01:46 UTC.
The requested start date was Friday, 06 November 2020 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 4-day, 9-hour, 59-minute period from Sunday, 01 November 2020 at 15:00 UTC to Friday, 06 November 2020 at 01:00 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 : smartcity OR smartcities
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:
[506] #iot,#iiot [450] smart,cities [406] marxist,revolution [292] smart,city [282] #rstats,#reactjs [185] #ai,#iot [184] #reactjs,#iot [169] #iiot,#iotpl [152] #ai,#rstats [150] #analytics,#ai Top Word Pairs in Tweet in G1:
[205] smart,cities [156] #iot,#iiot [141] #ai,#iot [71] take,look [71] use,cases [66] key,use [66] homes,built [66] built,#robot [65] 5g,already [65] already,deployed Top Word Pairs in Tweet in G2:
[50] smart,city [33] smart,cities [24] public,transportation [23] eliminate,network [23] network,downtime [23] downtime,public [23] transportation,systems [23] systems,here's [23] here's,jersey [23] jersey,transit Top Word Pairs in Tweet in G3:
[90] mohua_india,bvlfoundation [89] wriindia,wricitiesindia [85] bvlfoundation,wriindia [85] wricitiesindia,smartcities_hua [84] hardeepspuri,pib_india [84] pib_india,mib_india [84] mib_india,mohua_india [48] smartcities_hua,mohua_india [45] india,saudi [42] saudi,arabia Top Word Pairs in Tweet in G4:
[26] ceonoida,cmofficeup [26] cmofficeup,hardeepspuri [26] hardeepspuri,mohua_india [26] mohua_india,swachhbharat [26] swachhbharat,moefcc [25] moefcc,mygovindia [21] mygovindia,dainikbhaskar [17] dainikbhaskar,jagrannews [15] pramodb82117573,ceonoida [14] jagrannews,amarujalanews Top Word Pairs in Tweet in G5:
[239] #rstats,#reactjs [228] #iot,#iiot [154] #reactjs,#iot [131] #ai,#rstats [130] #analytics,#ai [71] #machinelearning,#ai [68] #analytics,#machinelearning [62] #linux,#serverless [53] #java,#rstats [53] harness,#5g Top Word Pairs in Tweet in G6:
[406] marxist,revolution [135] looks,marxist [135] revolution,acts [135] acts,marxist [135] revolution,same [135] same,goals [135] goals,marxist [134] libertytarian,looks [11] #un2030,#ubi [11] #ubi,#smartcity Top Word Pairs in Tweet in G7:
[19] happening,eu_sciencehub [19] eu_sciencehub,#research [19] #research,campuses [19] campuses,become [19] become,living [19] living,laboratories [19] laboratories,#smartcity [19] #smartcity,experiments [19] experiments,future [18] gabrielmariya,happening Top Word Pairs in Tweet in G8:
[16] #iot,#smartcities [13] #webinar,iot [13] iot,smart [13] smart,infrastructure [11] smart,cities [11] platform,smart [10] finalists,#smartcities [10] #smartcities,#government [10] #government,#utilities [10] #utilities,category Top Word Pairs in Tweet in G9:
[30] #smartcities,#smartcity [26] 46,trillion [24] business,opportunities [20] worth,46 [19] key,trends [19] trends,emerge [19] emerge,due [19] due,reshaping [19] reshaping,industries [19] industries,caused Top Word Pairs in Tweet in G10:
[16] smart,cities [13] smart,city [8] covid,19 [8] #smartcity,#mobility [6] waste,management [5] #iotforall,#iot [5] #iot,#smartcities [5] #smartcity,#innovation [4] second,installment [4] installment,#iotforall's Top Replied-To in Entire Graph:
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Top Mentioned in Entire Graph:
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Top Tweeters in Entire Graph:
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Top Tweeters in G10: