The graph represents a network of 2,952 Twitter users whose recent tweets contained "Hildburghausen", 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 Thursday, 26 November 2020 at 09:20 UTC.
The tweets in the network were tweeted over the 8-day, 19-hour, 14-minute period from Tuesday, 17 November 2020 at 13:55 UTC to Thursday, 26 November 2020 at 09:09 UTC.
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 : 2952
Unique Edges : 4274
Edges With Duplicates : 909
Total Edges : 5183
Number of Edge Types : 5
Retweet : 2834
Replies to : 380
Tweet : 1006
MentionsInRetweet : 503
Mentions : 460
Self-Loops : 1017
Reciprocated Vertex Pair Ratio : 0.00518806744487678
Reciprocated Edge Ratio : 0.0103225806451613
Connected Components : 492
Single-Vertex Connected Components : 401
Maximum Vertices in a Connected Component : 2192
Maximum Edges in a Connected Component : 4281
Maximum Geodesic Distance (Diameter) : 11
Average Geodesic Distance : 4.177762
Graph Density : 0.000444821882986705
Modularity : 0.630665
NodeXL Version : 1.0.1.441
Data Import : The graph represents a network of 2,952 Twitter users whose recent tweets contained "Hildburghausen", 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 Thursday, 26 November 2020 at 09:20 UTC.
The tweets in the network were tweeted over the 8-day, 19-hour, 14-minute period from Tuesday, 17 November 2020 at 13:55 UTC to Thursday, 26 November 2020 at 09:09 UTC.
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 : Hildburghausen
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:
[435] oh,schön [361] landkreis,hildburghausen [341] schulen,kitas [327] corona,hotspot [286] inzidenzwert,500 [263] #hotspot,#hildburghausen [256] besorgten,eltern [256] eltern,kinder [254] #hildburghausen,besorgten [254] kinder,notbetreuung Top Word Pairs in Tweet in G1:
[27] landkreis,hildburghausen [19] corona,hotspot [16] kreis,hildburghausen [12] #hildburghausen,#covidioten [11] inzidenz,500 [11] hildburghausen,thüringen [10] schulen,kitas [10] oh,schön [9] hotspot,hildburghausen [8] landkreis,#hildburghausen Top Word Pairs in Tweet in G2:
[136] oh,schön [125] corona,hotspot [124] inzidenz,500 [106] hotspot,#hildburghausen [103] rki,dashboard [102] #hildburghausen,inzidenz [102] 500,farbe [102] farbe,rki [102] dashboard,eingeführt [102] eingeführt,rosa Top Word Pairs in Tweet in G3:
[86] landkreis,hildburghausen [70] schulen,kitas [35] höchsten,inzidenz [34] landkreis,höchsten [34] nix,besseres [32] landkreis,#hildburghausen [29] corona,hotspot [28] 11,2020 [27] besorgte,eltern [26] thüringer,landkreis Top Word Pairs in Tweet in G4:
[169] vorstellen,wütend [169] wütend,heutigen [169] heutigen,demonstration [169] demonstration,#hotspot [169] #hotspot,#hildburghausen [169] #hildburghausen,besorgten [169] besorgten,eltern [169] eltern,kinder [169] kinder,notbetreuung [169] notbetreuung,sitzen Top Word Pairs in Tweet in G5:
[173] inzidenzwert,500 [172] gestern,meldete [172] meldete,rki [172] rki,410 [172] 410,todesfälle [172] todesfälle,hotspot [172] hotspot,spitzenreiter [172] spitzenreiter,hildburghausen [172] hildburghausen,inzidenzwert [172] 500,ziehen Top Word Pairs in Tweet in G6:
[80] corona,hotspot [77] ziehen,singend [75] #hildburghausen,corona [75] hotspot,deutschland [75] deutschland,inzidenzwert [75] inzidenzwert,527 [75] 527,gleicher [75] gleicher,ort [75] ort,abend [75] abend,etliche Top Word Pairs in Tweet in G7:
[119] landkreis,hildburghausen [115] schulen,kitas [68] treiber,infektionsgeschehens [67] hildburghausen,inzidenz [67] kitas,eindeutig [67] eindeutig,treiber [67] infektionsgeschehens,müller [65] thüringer,landkreis [65] 386,landrat [64] inzidenz,höhe Top Word Pairs in Tweet in G8:
[96] robert,koch [96] koch,institut [96] institut,meldet [96] meldet,tiefstwert [96] tiefstwert,#hildburghausen [96] #hildburghausen,gestern [96] gestern,abend [96] abend,niedrigste [96] niedrigste,empathie [96] empathie,wert Top Word Pairs in Tweet in G9:
[54] schulen,kitas [38] treiber,infektionsgeschehens [36] kitas,eindeutig [36] eindeutig,treiber [36] infektionsgeschehens,müller [31] neu,aufgetretenen [31] aufgetretenen,fällen [31] fällen,fast [31] fast,schüler [31] schüler,lehrer Top Word Pairs in Tweet in G10:
[28] 000,einwohner [27] 100,000 [24] super,hotspot [24] hotspot,hildburghausen [24] hildburghausen,pink [24] pink,liegt [24] liegt,inzwischen [24] inzwischen,603 [24] 603,#corona [24] #corona,fällen Top Replied-To in Entire Graph:
Top Replied-To in G2:
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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 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: