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Social Network Analysis reveals the alternative list of global power elite

Forbes magazine has been publishing the list of The World's Most Powerful People since 2009. The number of people in the list is proportional to the global population with the ratio being one slot for every 100 million people on Earth. When the list started in 2009, there were 67 people on the list and the latest list from year 2014 had 72 people. According for Forbes, the list is calculated based on the person’s influence over lots of other people (e.g. Pope Francis, Wal-Mart CEO, Doug McMillon), financial resources controlled by the people (e.g. GDP, market capitalization, profits, assets, revenues and net worth), power in multiple spheres (e.g. Bill Gates), active use of power by the people (e.g. Vladimir Putin).

While this list gives a snapshot of global ranking, it does not reveal information about past and present network connections and inter-linkages between people and organisations, the spread of power across the network, information about key entities who act as network intermediaries for power and which cluster of entities are most prominent in this global power structure. This is where we complement the Forbes data with LittleSis, which is a database of who-knows-who at the heights of business and government. LittleSis has information of the global rich and powerful such as past and present organizational affiliations (employment, directorships, memberships, alumni networks), donations (political contributions, grants), social connections (family ties, mentorships, friendships), professional connections (partnerships, supervisory relationships), services/contracts (legal representation, government contracts, lobbying services) etc.

We modeled the network of 72 most powerful people from the Forbes magazine 2014 list and their connections as a weighted undirected static multi-mode network (weighted by number of network connections). The data consisted of 2130 Nodes (entities) and 3730 Edges (network connections). We clustered the dataset and saw the emergence of 42 clusters. The network graph below shows the current and past links between each of the 72 people to various businesses, family ties, universities, political institutions, think tanks, organizations, board memberships, grants, partnerships, business connections etc. Each node depicts an entity and the lines between them depict the current or past connections.   

Network graph of 72 most powerful people from Forbes magazine 2014 and their network connections

The primary objective for using social network analysis for our analysis is to identify important actors/entities in the network. We use graph theory approaches to compute network centrality measures, which help us identify the entity’s structural relations and prominence in the network in the form of direct and indirect connections and visibility to other network entities. Degree Centrality is the simplest network measure and is measured by the number of ties, a node has. The weighted degree is computed based on the number of edges and the sum of the weight of the edges. In our result, the top five weighted centrality measures belong to Goldman Sachs, Jamie Dimon, The Partnership for New York City, Mario Draghi and IBM. This means that in our network, the maximum number of entities have a direct network connection to Goldman Sachs which could be any one of the earlier listed relationships such as organizational affiliations, donations, social or professional. Closeness centrality is a measure of how close the node is to all other nodes and which nodes can reach others quickly. In our result, the top five weighted centrality measures belong to Bernard Arnault, Citizens for a Sound Economy, Charles G. Koch Charitable Foundation, David Cameron and News of the World. This means that in our network, Bernard Arnault is the most closely connected entity to the rest of the network. Betweeness centrality is a measure of the node connecting many other nodes in the network. Based on our analysis, the top five betweeness centrality measures belong to Goldman Sachs, IBM, Microsoft Corporation, White House and Brookings Institution. This means that IBM is one of the top five entities in our network, which ensures the shortest path of connection to any other entity in the network. Eigenvector centrality is a measure of the influence of a node in a network as a function of the centralities of its neighbors. One variant of the eigenvector centrality measure is the Google's PageRank algorithm. Based on our analysis, the top five eigenvector centrality measures belong to Goldman Sachs, Brookings Institution, IBM, Microsoft Corporation and The Partnership for New York City. This means that Brookings Institution is one of the top five entities in our network, which in turn is connected to the other powerful notes in our network.

Top 20 entities listed on decreasing order of network centrality measures

Weighted Degree

Closeness

Betweeness

Eigenvector

Goldman Sachs

Bernard Arnault

Goldman Sachs

Goldman Sachs

Jamie Dimon

Citizens for a Sound Economy

IBM

Brookings Institution

The Partnership for New York City

Charles G. Koch Charitable Foundation

Microsoft Corporation

IBM

Mario Draghi

David Cameron

White House

Microsoft Corporation

IBM

News of the World

Brookings Institution

The Partnership for New York City

Microsoft Corporation

The Conservative Party (British)

Council on Foreign Relations

Council on Foreign Relations

TED

Halcyon Molecular

David Koch

White House

David Koch

Elizabeth R Koch

The Partnership for New York City

Obama for America

The World Bank Group

Chase Koch

Google Inc.

Fix the Debt

Google Inc.

Elizabeth B Koch

Fix the Debt

Citigroup Inc.

Council on Foreign Relations

Vonda Holliman

JPMorgan Chase and Co.

JPMorgan Chase and Co.

Lakshmi Mittal

Fred C Koch

The World Bank Group

The World Bank Group

Brookings Institution

Thomas J Pyle

Institute for Advanced Study

Atlantic Council

Rupert Murdoch

Mike Morgan

Obama for America

Stephen Friedman

Bill Gates

David Snyderman

Citigroup Inc.

John C Whitehead

White House

James L Mahoney

Bill and Melinda Gates Foundation

Robert E Rubin

Bill Clinton

Steven L Packebush

Koch Industries, Inc.

Lloyd C Blankfein

Institute for Advanced Study

Steven J Feilmeier

The Aspen Institute

Google Inc.

Janet L Yellen

John C Pittenger

Atlantic Council

Harvard University

Barack Obama

Jeffrey N Gentry

Executive Branch

Federal Reserve Bank of New York

The network snapshot of top ten clusters is given below. 

Network Graph segment of Cluster 1

Network Graph segment of Cluster 2

Network Graph segment of Cluster 3

Network Graph segment of Cluster 4

Network Graph segment of Cluster 5

Network Graph segment of Cluster 6

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Comment by Hanson Wu on September 15, 2015 at 9:26am

Great work, Dr. Jai Ganesh ! Could you let me know which tool you use to generate these beautiful graphs?

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