The graph represents a network of 2,779 Twitter users whose tweets in the requested range contained "kaggle", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 03 October 2021 at 04:27 UTC.
The requested start date was Sunday, 03 October 2021 at 00:01 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, 23-hour, 58-minute period from Sunday, 19 September 2021 at 00:01 UTC to Sunday, 03 October 2021 at 00:00 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 : 2779
Unique Edges : 1909
Edges With Duplicates : 6382
Total Edges : 8291
Number of Edge Types : 5
Replies to : 635
Mentions : 916
Retweet : 2152
MentionsInRetweet : 2781
Tweet : 1807
Self-Loops : 1825
Reciprocated Vertex Pair Ratio : 0.0444915254237288
Reciprocated Edge Ratio : 0.0851926977687627
Connected Components : 676
Single-Vertex Connected Components : 512
Maximum Vertices in a Connected Component : 1679
Maximum Edges in a Connected Component : 6673
Maximum Geodesic Distance (Diameter) : 13
Average Geodesic Distance : 4.14822
Graph Density : 0.000447017135354612
Modularity : 0.386578
NodeXL Version : 1.0.1.447
Data Import : The graph represents a network of 2,779 Twitter users whose tweets in the requested range contained "kaggle", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 03 October 2021 at 04:27 UTC.
The requested start date was Sunday, 03 October 2021 at 00:01 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, 23-hour, 58-minute period from Sunday, 19 September 2021 at 00:01 UTC to Sunday, 03 October 2021 at 00:00 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 : kaggle
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:
[370] machine,learning [212] kaggle,competition [136] 30,days [135] getting,started [131] kaggle's,30 [130] days,machine [130] learning,competition [130] competition,made [130] made,several [128] #datascience,#machinelearning Top Word Pairs in Tweet in G1:
[25] machine,learning [23] kaggle,competition [19] course,kaggle [18] data,science [14] kaggle,grandmasters [13] competition,community [13] community,insights [13] insights,nvidia [13] nvidia,kaggle [12] developer,blog Top Word Pairs in Tweet in G2:
[88] schedule,notebooks [88] notebooks,run [88] daily,weekly [88] weekly,monthly [81] run,daily [81] monthly,kaggle [81] kaggle,read [81] read,more [81] more,announcement [80] kaggle,schedule Top Word Pairs in Tweet in G3:
[37] kaggleで使う便利なrepoは私はこの辺かな,classification [37] classification,timm [37] timm,detection [37] detection,yolox [37] yolox,another [37] another,efficientdet [37] efficientdet,adelaidet [37] adelaidet,segmentation [37] segmentation,semantic [36] zfphalanx,kaggleで使う便利なrepoは私はこの辺かな Top Word Pairs in Tweet in G4:
[115] #datascience,#machinelearning [110] machine,learning [73] #ai,#ml [50] learning,kaggle [49] #python,#kaggle [46] kaggle,competition [45] best,competitive [45] competitive,coding [45] coding,websites [45] websites,machine Top Word Pairs in Tweet in G5:
[122] machine,learning [120] 30,days [120] getting,started [118] kaggle's,30 [118] days,machine [118] learning,competition [118] competition,made [118] made,several [113] several,videos [113] videos,notebooks Top Word Pairs in Tweet in G6:
[112] قبل,٢٠ [112] ٢٠,عام [112] عام,كان [112] كان,وبكل [112] وبكل,فخر [112] فخر,يبيع [112] يبيع,الفاكهة [112] الفاكهة,بدكانه [112] بدكانه,الصغير [112] الصغير,بحائل Top Word Pairs in Tweet in G7:
[6] #un,#ai [4] deep,learning [4] learning,changing [4] changing,corporate [4] corporate,finance [4] finance,around [4] around,world [4] world,lucamassaron [4] lucamassaron,associationofd4 [4] associationofd4,#deeplearning Top Word Pairs in Tweet in G8:
[44] 2022,#bigdatabowl [44] #bigdatabowl,live [27] live,sign [27] sign,download [27] download,data [26] statsbylopez,2022 [18] demo,code [17] live,nfl [17] nfl,football [17] football,operations Top Word Pairs in Tweet in G9:
[39] kaggle界隈では複数シードのアンサンブルとかにまつわる話,cv界隈でのランダムシード数と精度の関係を分析する論文 [39] cv界隈でのランダムシード数と精度の関係を分析する論文,シード数によって生じる精度の差は有意と言える物で [38] goto_yuta_,kaggle界隈では複数シードのアンサンブルとかにまつわる話 [38] シード数によって生じる精度の差は有意と言える物で,多くの研究がラッキシードによるものではないかと著者が懐疑的な姿勢を示してい [12] 就活してた時に五反田の某it企業のエンジニアに,オレなんかジュパイターノートブックとかでkaggle始めて今では銅とれるようになったよ [12] オレなんかジュパイターノートブックとかでkaggle始めて今では銅とれるようになったよ,って死ぬほどドヤ顔で言われた時本当に困った [12] って死ぬほどドヤ顔で言われた時本当に困った,そして志望度は完全に0になった [12] そして志望度は完全に0になった,社名はガチで忘れたけど [11] goto_yuta_,就活してた時に五反田の某it企業のエンジニアに [11] 社名はガチで忘れたけど,この Top Word Pairs in Tweet in G10:
[27] thought,using [27] using,tensorflow [27] tensorflow,decision [27] decision,forests [27] forests,wanted [27] wanted,hyperparameter [27] hyperparameter,tuning [27] tuning,#keras [27] #keras,tuner [26] tensorflow,thought 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 G7:
Top Replied-To in G8:
Top Replied-To in G9:
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: