Home · Russia Politics Analysis

Network at a glance

Nodes	Edges	Density	Avg. degree
355	470	0.0075	2.65
people, bodies, companies and parties	recorded relationships	share of possible links present	links per node

The graph holds 355 nodes across 24 connected components; 300 nodes sit in the largest one (84.5%) and 9 are currently isolated — present in the node list but with no relationship recorded yet.

Data quality: 0 duplicate edge groups (0 redundant rows), 0 self-loops, and 0 CSV rows that collapse into an existing undirected edge.

People vs institutions

What the ties join

Edge ends	Count	Share
person–organisation	407	86.6%
person–person	44	9.4%
organisation–organisation	19	4.0%

Only 9.4% of ties run person-to-person; 86.6% run person-to-organisation. This is overwhelmingly a network of offices rather than of individuals.

Does like connect to like?

Assortativity by node type	-0.5236
People in the graph	158
Person-to-person ties	44
Density among people only	0.003548
Isolated people	9

Type assortativity is -0.5236, i.e. strongly negative — people and bodies connect across type, not within it.

How concentrated is connectivity

Degree distribution

Gini coefficient	0.4872
Top 10 nodes' share of all links	0.2191
Median degree	1
Mean degree	2.648

A Gini near 0 would mean every node holds about the same number of ties; near 1, a handful hold almost all of them.

Nodes by degree

Links held	Nodes
0 links	9
1 links	169
2–4 links	139
5–9 links	29
10+ links	9

Global clustering 0.0129 — the share of "friend of a friend is also a friend" triangles that actually close. Low values mean ties do not form closed circles.

Communities — who clusters together

The 40 communities detected by greedy modularity, largest first. The type mix and member names matter more than the count: a community of one company plus its officials is a different finding from one spanning several ministries.

#	Size	Type mix	Members
1	49	person 32, company 12, government 5	Alexander Shulgin, Alexei Miller, Alexei Mordashov, Alexey Evgenyevich Likhachev, Andrei Akimov, Andrei Bokaryov, Andrei Guryev, Andrei Kostin, Andrei Melnichenko, Andrei Ryumin …
2	42	person 24, government 14, company 4	Alexander Grushko, Alexander Khloponin, Alexander Tkachev, Alexey Gromov, Anatoly Chubais, Anton Siluanov, Anton Vaino, Bank of Russia, CSKA Moscow Football Club, Credit Agricole Corporate and Investment Bank …
3	32	person 16, government 14, company 1	Alexander Voloshin, Anna Kuznetsova, Association of Organizations for Family Protection, Dmitrii Medvedev, Dmitrii Patrushev, Dmitry Kobylkin, Duma, Elena Mizulina, Federation Council, Fund 'Pokrov' …
4	30	government 20, person 10	Adam Delimkhanov, Anna Tsivileva, Anton Alikhanov, Armed Forces of the Russian Federation, Dyumin A.G., Evgeniy Murov, Federal Customs Service of the Russian Federation, Federal National Guard Service of the Russian Federation, Federal Protective Service of the Russian Federation, Government of Kaliningrad Oblast …
5	21	person 10, government 7, company 4	Alexander Novak, Chelyabinsk Region, City Administration of Norilsk, Evraz, Kazakhmys, Mikhail Prokhorov, Ministry of Energy of the Russian Federation, Norilsk Nickel, Oleg Deripaska, Pharmstandard
6	21	government 15, person 6	Alexander Shokhin, Alexei Kudrin, Alexey Ulyukayev, Andrei Belousov, Arkady Dvorkovich, Baltika Brewery, Central Economic and Mathematical Institute of the Russian Academy of Sciences, Federal Commission on the Securities Market of Russia, Gleb Nikitin, Institute of Economics and Forecasting of Scientific and Technological Progress of the USSR Academy of Sciences
7	18	person 9, company 8, government 1	Alexey Vertyashkin, Alfa, Anastasia Rakova, BP, Eduard Khudainatov, FSB of Russia, Feoktistov Alexander Vladimirovich, Gerhard Schroder, Gunvor, Mikhail Fridman
8	15	government 12, person 3	Andrey Turchak, Dmitry Rogozin, Federation Council of the Federal Assembly of the Russian Federation, Konstantin Malofeev, MDM Bank, MarCap Investment Group, Marshall Capital Partners, Pskov Oblast, Regent European Securities, Renaissance Capital

When ties formed

217 of 470 edges carry a start date (253 do not). Bars are scaled to the busiest year shown.

Year	Edges starting
2014	8
2015	13
2016	14
2017	1
2018	5
2019	7
2020	18
2021	4
2023	2
2024	10
2025	1
2026	1

Fragility and organisational distance

Structural weak points

Bridges (edges whose removal disconnects)	181
Articulation points (single points of failure)	66

Bridges: Alexander Shokhin <-> Moscow State University, Alexander Shokhin <-> Research Institute of Economics under the State Planning Committee of the USSR, Alexander Shokhin <-> Research Institute of Labor of the State Committee for Labor of the USSR, Alexander Shokhin <-> Institute of Employment Problems of the USSR Academy of Sciences and State Committee for Labor of the USSR, Alexander Shokhin <-> Ministry of Labor of the RSFSR, Alexander Shokhin <-> Russian Agency for International Cooperation and Development, Alexander Shokhin <-> National Research University Higher School of Economics, Alexander Shokhin <-> Federal Commission on the Securities Market of Russia

Articulation points: Adam Delimkhanov, Alexander Shokhin, Alexei Mordashov, Alexey Evgenyevich Likhachev, Alfa, Alisher Usmanov, Andrei Guryev, Andrey Turchak

Mean organisational distance by type

Node type	Mean closeness	n
person	3.76	443
government	3.75	337
company	3.74	93
party	3.86	7

The closeness column encodes distance in the org chart; a lower mean means inner-circle access.

Weighted connectivity

Degree summed over the closeness weight, so a 2-hop tie counts more than a 5-hop one. Compare with the raw-degree table: the ordering changes.

#	Node	Type	Weighted degree
1	Russian Union of Industrialists and Entrepreneurs	government	141
2	Government of the Russian Federation	government	129
3	Alexander Shokhin	person	82
4	Feb 24 meeting	government	70
5	Presidential Administration	government	69
6	Vladimir Putin	person	59
7	Sergey Aksenov	person	40
8	Security Council of the Russian Federation	government	37
9	Gazprom	company	33
10	Konstantin Malofeev	person	33

What the network is made of

Node types

Type	Count	Share
government	163	45.9%
person	158	44.5%
company	33	9.3%
party	1	0.3%

Relationship types

Type	Count	Share
official	278	59.1%
manager	57	12.1%
business	48	10.2%
ownership	42	8.9%
manual	30	6.4%
family	10	2.1%
friendship	5	1.1%

Most connected nodes

Top 20 by degree — the raw count of recorded relationships. A high score means a figure or body sits at the centre of many formal ties.

#	Node	Type	Degree
1	Russian Union of Industrialists and Entrepreneurs	government	36
2	Feb 24 meeting	government	35
3	Government of the Russian Federation	government	33
4	Alexander Shokhin	person	21
5	Presidential Administration	government	21
6	Vladimir Putin	person	20
7	Security Council of the Russian Federation	government	11
8	Andrei Belousov	person	10
9	Sergey Aksenov	person	10
10	Igor Sechin	person	9
11	Konstantin Malofeev	person	9
12	Maxim Oreshkin	person	9
13	Rosneft	company	9
14	Tekzler Alexey	person	9
15	Dyumin A.G.	person	8
16	Gazprom	company	8
17	Mikhail Kovalchuk	person	8
18	Ozero Dacha Collective	company	8
19	St Petersburg	government	8
20	Alexey Evgenyevich Likhachev	person	7

Read with care: the top two entries are institutional / event nodes — an organisation or a meeting accumulates edges by definition, so it outranks individuals on raw degree. Compare the betweenness table below before drawing conclusions about personal influence.

Brokers and gatekeepers

Top 10 by betweenness centrality — how often a node lies on the shortest path between two others. High scores mark figures who bridge otherwise separate clusters, i.e. the people information must pass through.

#	Node	Type	Betweenness
1	Government of the Russian Federation	government	0.2930
2	Vladimir Putin	person	0.1539
3	Alexander Shokhin	person	0.1468
4	Presidential Administration	government	0.1131
5	Russian Union of Industrialists and Entrepreneurs	government	0.1065
6	Feb 24 meeting	government	0.1018
7	Gazprom	company	0.0789
8	German Gref	person	0.0606
9	Andrei Belousov	person	0.0588
10	Sergey Aksenov	person	0.0498

Network structure

Average shortest path	4.708	degrees of separation within the giant component
Diameter	12	longest shortest path
Modularity	0.7097	how cleanly the graph splits into communities (0–1)
Communities	40	clusters detected by greedy modularity
Max degree	36	edges held by the single most-connected node

The isolate backlog

9 nodes sit in the node list with no relationship recorded. These are genuine gaps in the data — their names appear nowhere in edges.csv — not name-matching failures. Filling them in is the highest-value data work available, because an unconnected oligarch is invisible to every centrality measure on this page.

Node type	Isolated nodes
person	9

The isolate set includes major figures — Rybolovlev, Fedun, Prokhorov, Rashnikov, Lisin, Kantor, Karapetyan, Dvorkovich, Rogozin and Novak among them. They are tracked in the local curation dashboard (isolate_curation.py), which proposes ranked candidate endpoints for each one so a connection is a confirmation rather than a blank-page exercise.