I have been interested in applying some tools for social network analysis visualization to merit transactions, and finally I have had some time to analyze global structure of merit networks.
In the gif movie above and figures below, forum accounts and their merit transactions are expressed by nodes and edges, respectively. In the above gif movie, time evolution of every nodes are governed by attractive and repulsive force which are calculated from the number of total sent merits between accounts by using ForceAtlas2
[1]. The nodes are classified and given different colors depending on the communities they belong to, which are identified by using the Louvain method
[2]. These methods visualize the strength of merit connections explicitly, and detects its substructure algorithmically.
After waiting for a while for stabilization of the system, we obtain the global structure of merit networks:
We see there are several communities in the forum. Accounts in the same communities have close relationship through merit transactions. Let us take a closer look of each community.
Purple (theymos, suchmoon, Vod,...): mixture of English boards such as Bitcoin, Altcoin, Meta, Beginners & Help etc. and small local boards
Green (JayJuanGee, LastoftheV8s, vapourminer,...): Economy
Purple and green groups are the largest and closely connected groups. While purple looks like a mixture of several sections, somehow Economy section is deteceted as an independent goup, as there are certain people who focus on Economy section. Small local boards such as Japanese board are included in the purple group.
Other groups are top 3 largest local boards.
Black (dbshck, pandukelana2712,...): Local > Bahasa Indonesia (Indonesian)
Orange (EFS, AlyattesLydia,...): Local > Türkçe (Turkish)
Green (Ranyar, RuSS512,...): Local > Pyccкий (Russian)
Red (Co1n, poptop,...) : Local > Pyccкий (Russian)
Green and Red are both Russian community implying that it is also sufficiently large to exhibit its substructure. From the previous analysis by zentdex
[3], the top 3 merit distributed local boards are Russian, Turkish, Indonesian local boards. The above result is thus consistent. Furthermore, the above analysis sheds light on their relative distance from other boards: Among the three local boards, Russian community is the largest but at the same time the most far from the English boards. On the contrary, Indonesian local board is the smallest among the three, but nearest to the English boards, implying that they are sending merits each other.
We note that there are several large nodes that do not belong to a community but has different colors from their vicinity: paxmao, Ognasty, ui_zakharchenko, explorder, xandry fall into such a class. Why they are independent from any group?
The interpretation is that their merit histories are peculiar and different from others.
paxmao and
Ognasty look like sending merits to a lot of sections, which is why the algorithm could not tell which communities they are belonging to. It implies that they have been trying hard to distribute merits to the entire forum.
On the other hand,
explorder and
ui_zakharchenko are the OPs of the explanations of merit system
[4] and famous bounty managers
[5], which are very popular in Russian local board. Since their merit transactions are mainly within those threads, they respectively form independent groups.
Finally,
xandry has sent most of smerits to "(Deleted/Off-limits/Ignored)" so I assumed his/her transactions also mainly remain within a single topic, but it is still not clear to me how to interpret his/her merit history:
I assumed he/she is focusing on some particular thread and evaluating users posted there, but it is not clear to me why he/she needs to send merits so frequently. Maybe some very active thread was deleted recently together with a lot of posts which were merited? In any case it is indeed a special case, which is automatically revealed by the above algorithm.
In these cases, they are recognized as small independent communities and have different colors. However, they are moderately belonging to English and Russian communities which can be read off from their distance to those communities.
Of course, a related analysis is the wonderful analysis and implementation by DdmrDdmr
[3], which allows us to check personal merit networks very easily. The thread appeared when I was learning and preparing this analysis, and I was actually afraid that nothing was left for further work after his/her analysis. Fortunately the above analysis for global structure seems complementary to his/her work. Now we know personal and global structures of merit networks.
References:
[1] M. Jacomy , T. Venturini, S. Heymann, M. Bastian, "ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software",
PloS one 9 (6), e98679 (2014).
[2] V. Blondel, J. Guillaume, R. Lambiotte, E. Mech, "Fast unfolding of communities in large networks",
J. Stat. Mech. P10008 (2008),
[arXiv:0803.0476] (
http://findcommunities.googlepages.com).
[3]
zentdex, "Where the Merit Pours?"
,
Bitcointalk, 3093768, March 09, (2018).
[4]
explorder, "Merit - чтo этo и кaк этим пoльзoвaтьcя",
Bitcointalk, 2818398, January 24 (2018).
[5]
ui_zakharchenko, "Пoлeзнocти для бayнтиcтoв",
Bitcointalk, 2845483, January 29, (2018).
[6]
DdmrDdmr, "Our very own sMerit Network Picture",
Bitcointalk, 3395255, April 25 (2018).
