Hive Reputation Explained: Part2 - HIVE POWER & HIVE REPUTATION

Hey All,

Hive Power and Reputation Analysis.png

This post is in continuation with my Yesterdays post - Hive Reputation Explained: Step-by-Step Conversion Guide - with an EXAMPLE..., where we saw Hive users who had a reputation close to my Hive reputation which is 79.3. This data was not sufficient to conclude to anything and hence this part2 post where we fill further deep dive into looking these users which are close to my reputation what their #HIVE Power aka HP is and at the same time we will see #HIVE users closest to my Hive Power and their Reputation. I know it may sound some what confusing but trust me when you see that data and table it would be intresting to analyze, compare, and truly understand ONEs position among others. Ok here is the the DBeaver UI where I had the query working. And then we have the output of the query in table form.

Top 10 Hive users closest to my Reputation & their Hive Power

Name	Reputation (raw)	Rep Score	Hive Power (VESTS)	Hive Power (HP)
*gungunkrishu*	*1074557065212202*	*79.28106535596065*	*233473354.351516*	*140540.73775321752*
stayoutoftherz	1082988226441942	79.31161361750247	747335301.737578	449863.13297700154
teamukraine	1064252086102883	79.24340059122466	13157101.184945	7919.998889645867
bengy	1064212948383316	79.24325684851965	10103351.685370	6081.775385401247
ssekulji	1089679392599398	79.33568864058742	18212132.345870	10962.906337029657
steemflow	1092312324349458	79.34512150367976	60228927.162746	36255.17730298614
omarcitorojas	1092591668552799	79.34612096070312	3611579.817376	2174.0129335035517
creativecrypto	1055131252444029	79.20975838125304	0.368483	0.0002218106336518
cryptopassion	1095186992075950	79.35539449198377	8533.824072	5.136988476763447
susanli3769	1096957439065679	79.3617079990954	95772191.295858	57650.66621134431
eddiespino	1050927028586630	79.1941530554422	12771610.137349	7687.950155961106

I leveraged Python script using Pandas and Matplotlib to visualize ythe above table. You can clearly see that my reputation score which is (79.28) places me right in the middle of other only one top accounts with a Hive Power of stayoutoftherz (449K HP). At the same time there are 5 users whoes reputation and Hive Power is almost close to each other. One One extreme case where creativecrypto has almost no Hive Power (0 HP) but still a comparable rep score of (79.20). Overall we can say that users can have similar reputation but widely different influence in terms of HP.

And here is the working Query to retriev records users closest to your reputation and the users Hive Power.

WITH globals AS (
    SELECT 
        CAST(total_vesting_shares AS FLOAT) AS total_vests,
        CAST(total_vesting_fund_hive AS FLOAT) AS total_hive_fund
    FROM DynamicGlobalProperties
),
base AS (
    SELECT 
        a.name,
        a.reputation,
        CASE 
            WHEN a.reputation = 0 THEN 25
            ELSE 
                (LOG10(CAST(ABS(a.reputation) AS FLOAT)) - 9) 
                * (CASE WHEN a.reputation > 0 THEN 1 ELSE -1 END) * 9 + 25
        END AS rep_score,
        a.vesting_shares,
        g.total_vests,
        g.total_hive_fund
    FROM Accounts a
    CROSS JOIN globals g
)
SELECT TOP 11 
    name,
    reputation,
    rep_score,
    vesting_shares AS hive_power_vests,
    (vesting_shares / total_vests) * total_hive_fund AS hive_power_hp
FROM base
ORDER BY ABS(rep_score - (SELECT rep_score FROM base WHERE name = 'gungunkrishu'));

Next I didnt stop here and went on to query records with a slight change. I wanted to see users reputation when compared to my Hive Power. Interesting, isn’t it?.

Top 10 Hive users closest to my Hive Power and their Reputation....

Name	Hive Power (VESTS)	Hive Power (HP)	Raw Reputation	Readable Reputation
livinguktaiwan	237426177.371026	142920.08346503	1412963888039479	80.35117956220648
deepresearch	234499614.910854	141158.42198480	131269668168915	71.06347948668746
bitcointalker	234000000.000000	140857.67585162	0	25.00000000000000
*gungunkrishu*	*233473354.351516*	*140540.65840699*	*1074557065212202*	*79.28106535596065*
revisesociology	233382641.885788	140486.05350488	1903459948240358	81.51589368713266
moderator	232557143.000000	139989.13959689	0	25.00000000000000
peakd	232211483.884969	139781.06806022	292225183600292	74.19145876148988
delegate.lafona	232193959.460069	139770.51912962	1604913152392	53.84906382529933
giuatt07	231356352.739348	139266.31683920	481416188174986	76.14268620853996
gengua	229646455.012197	138237.03384864	12380977244976	61.83479432866368

This is what I get when I plot the above table information using Phython. I got only one user whoes Hive Power and reputation score is close to me. Rest users have a scatterd Hive power and reputation score. Two users having greater HP and score than me and rest had lesser HP and score.

Working Query to retriev records users closest to your Hive Power and their reputation

WITH globals AS (
    SELECT 
        CAST(total_vesting_shares AS FLOAT) AS total_vests,
        CAST(total_vesting_fund_hive AS FLOAT) AS total_hive_fund
    FROM DynamicGlobalProperties
),
base AS (
    SELECT 
        a.name,
        a.vesting_shares,
        a.reputation,
        CASE 
            WHEN a.reputation = 0 THEN 25
            ELSE 
                (LOG10(CAST(ABS(a.reputation) AS FLOAT)) - 9) 
                * (CASE WHEN a.reputation > 0 THEN 1 ELSE -1 END) * 9 + 25
        END AS rep_score,
        g.total_vests,
        g.total_hive_fund
    FROM Accounts a
    CROSS JOIN globals g
),
closest AS (
    SELECT TOP 10 
        name,
        vesting_shares AS hive_power_vests,
        (vesting_shares / total_vests) * total_hive_fund AS hive_power_hp,
        reputation AS raw_reputation,
        rep_score AS readable_reputation
    FROM base
    ORDER BY ABS(vesting_shares - (SELECT vesting_shares FROM base WHERE name = 'gungunkrishu'))
)
SELECT *
FROM closest
ORDER BY hive_power_hp DESC;

Overall, it was a great learning experience to pull records and explore Hive users with Reputation Scores and Hive Power similar to mine. It’s always insightful to see how these two metrics work together in reflecting an account’s influence and growth on Hive.

That’s all for today’s post on “Hive Reputation Explained: Part 2 – Hive Power & Reputation Analysis.” I hope I was able to explain the concept clearly. If you have any follow-up questions or doubts, please feel free to share them in the comments below.Happy Learning with HiveSQL....Cheers...

Hive Reputation Explained: Part2 - HIVE POWER & HIVE REPUTATION - Analysis...

#Hive #Blockchain #Crypto #HiveSQL #Reputation #HiveCommunity #Web3 #Decentralization #HiveBlog

Best Regards
Paras

Image Courtesy:: pro canva license, hiveblocks, DBeaver

PS:- None of the above is a FINANCIAL Advice. Please DYOR; Do your own research. I've have an interest in BlockChain & Cryptos and have been investing in many emerging projects.