The graph represents a network of 1,547 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 04 September 2022 at 02:02 UTC.
The requested start date was Sunday, 04 September 2022 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 5-day, 0-hour, 38-minute period from Sunday, 28 August 2022 at 09:39 UTC to Friday, 02 September 2022 at 10:17 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 : 1547
Unique Edges : 2061
Edges With Duplicates : 14717
Total Edges : 16778
Number of Edge Types : 5
Retweet : 6047
MentionsInRetweet : 7695
Mentions : 1925
Tweet : 1078
Replies to : 33
Self-Loops : 1576
Reciprocated Vertex Pair Ratio : 0.0371966432297573
Reciprocated Edge Ratio : 0.0717253444128581
Connected Components : 37
Single-Vertex Connected Components : 21
Maximum Vertices in a Connected Component : 1464
Maximum Edges in a Connected Component : 16658
Maximum Geodesic Distance (Diameter) : 7
Average Geodesic Distance : 3.188908
Graph Density : 0.00191205947997669
Modularity : 0.193929
NodeXL Version : 1.0.1.504
Data Import : The graph represents a network of 1,547 Twitter users whose tweets in the requested range contained "#FinServ", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Sunday, 04 September 2022 at 02:02 UTC.
The requested start date was Sunday, 04 September 2022 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 5-day, 0-hour, 38-minute period from Sunday, 28 August 2022 at 09:39 UTC to Friday, 02 September 2022 at 10:17 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 : #FinServ
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:
[1058] #finserv,#cloud [1011] #ehealth,#finserv [992] #healthtech,#datascientist [989] #datascientist,#csuite [989] #csuite,#digitalhealth [977] #digitalhealth,#web3 [970] #cx,#ehealth [967] #web3,#cx [842] #iot,#iiot [773] #python,#iot Top Word Pairs in Tweet in G1:
[267] #fintech,#finserv [169] #web3,#technology [123] #finserv,#fintech [116] #technology,#nftcommunity [114] #finserv,#banking [110] #aiethics,#machinelearning [96] #fintech,#insurtech [90] #machinelearning,#python [85] #nftcommunity,#lunc [82] #proptech,#fintech Top Word Pairs in Tweet in G2:
[694] #ehealth,#finserv [687] #finserv,#cloud [682] #healthtech,#datascientist [682] #cx,#ehealth [680] #datascientist,#csuite [680] #csuite,#digitalhealth [675] #web3,#cx [674] #digitalhealth,#web3 [439] #cloud,#insurtech [358] chidambara09,#healthtech Top Word Pairs in Tweet in G3:
[179] #aiethics,#machinelearning [142] #machinelearning,#python [125] #web3,#technology [118] #fintech,#finserv [111] #digitalassets,#fintech [94] #technology,#nftcommunity [83] #python,#deeplearning [81] #nftcommunity,#lunc [72] #deeplearning,#lunc [59] #finserv,#cloud Top Word Pairs in Tweet in G4:
[707] #python,#iot [693] #iot,#iiot [657] #javascript,#reactjs [591] #iiot,#pytorch [575] #reactjs,#wordpress [507] #wordpress,#security [482] #security,#defi [427] #business,#investing [398] #pytorch,#ad [381] #ad,#javascript Top Word Pairs in Tweet in G5:
[128] #healthtech,#datascientist [127] #datascientist,#csuite [127] #csuite,#digitalhealth [127] #ehealth,#finserv [125] #finserv,#cloud [124] #digitalhealth,#web3 [122] chidambara09,#healthtech [113] #web3,#cx [111] #cloud,#insurtech [106] #cx,#ehealth Top Word Pairs in Tweet in G6:
[38] uv,tattoos [38] tattoos,glow [38] glow,dark [38] dark,gigadgets_ [38] gigadgets_,#ehealth [38] #ehealth,#cx [38] #cx,#payments [38] #payments,#finserv [38] #finserv,#iot [38] #iot,#digital Top Word Pairs in Tweet in G7:
[14] #finserv,#financialservices [12] #fintech,#infographic [10] #financialservices,#finance [7] financial,services [7] #artificialintelligence,#ml [7] #ml,#finserv [7] #finance,#finserv [7] #infographic,#finance [7] #fintech,#finserv [6] #ai,impact Top Word Pairs in Tweet in G8:
[31] open,finance [31] finance,impacting [31] impacting,#finserv [31] #finserv,industry [31] industry,panel [31] panel,discussion [31] discussion,leaders [31] leaders,forrester [30] awsmarketplace,open [30] forrester,yapilyltd Top Word Pairs in Tweet in G9:
[4] financial,institutions [4] institutions,state [4] state,improving [4] improving,customer [4] customer,experience [4] experience,top [4] top,corporate [4] corporate,priority [3] windowontech,financial [3] priority,increasing Top Word Pairs in Tweet in G10:
[8] #csuite#digitalhealth,#web3#cx [8] #web3#cx,#ehealth [8] #ehealth,#finserv [8] #finserv,#cloud [4] app,link [4] link,#belgiangp [4] #belgiangp,#susijengi [4] #susijengi,#pesis [4] #pesis,#naisleijonat [4] #naisleijonat,#fintech Top Replied-To in Entire Graph:
Top Replied-To in G1:
Top Replied-To in G2:
Top Replied-To in G4:
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: