From crowds to power couples, network science reveals the hidden structure of community dynamics :: InvestMacro

By Mayank KejriwalAnd the University of Southern California

The world is a networked place, literally and figuratively. The field of network science today is used to understand diverse phenomena such as The spread of misinformationAnd the West African trade And the Protein-protein interactions in cells.

Network science has revealed many Universal Characteristics Complex social networks, which in turn made it possible to know the details of certain networks. For example, the network formed by the international financial corruption scheme exposed Panama Papers Achievement he have Unusual lack of connections between its parts.

But understanding the hidden structures of the main elements of social networks, such as subgroups, has remained elusive. My colleagues and I have found two complex patterns in these networks that can help researchers better understand the hierarchy and dynamics of these elements. We’ve found a way to discover powerful “inner circles” in large organizations simply by studying the networks that determine which emails are sent between employees.

We demonstrated the usefulness of our methods by applying them to the popular Enron network. Enron was an energy trading company Fraud committed on a large scale. our study He also showed that the method could potentially be used to detect people who wield enormous soft power in an organization regardless of their title or official position. This may be useful for historical, social and economic research, as well as governmental, legal, and media investigations.

From pen and paper to artificial intelligence

Sociologists have built and studied smaller social networks in accurate field experiments of At least 80 years old, long before the advent of the Internet and online social networks. The concept is so simple that it can be drawn on paper: the entities of interest – people, companies, countries – are nodes represented as points, and the relationships between pairs of nodes are links represented as lines drawn between points.

Two sets of dots with lines connecting some dots
An abstract grid, to the left, shows lines between points that represent relationships. The grid on the right shows a small portion of a true network of West African merchants, based on data from Oliver J. Walther.
Mayank KejriwalAnd the CC BY-ND

The use of network science to study human societies and other complex systems has gained new meaning in late nineties When researchers discovered some universal properties of networks. Some of these characteristics are universal It has since entered mainstream popular culture. One concept is Six Degrees of Kevin Bacon, based on the famous empirical finding that any two people on Earth Six or fewer links apart. Likewise, versions of phrases such as “The rich are getting richer” And the “The winner takes it allIt has also been replicated in some networks.

These global properties, that is, those that apply to the entire network, seem to emerge from the short-sighted and local actions of the independent nodes. When I connect with someone on LinkedIn, I certainly don’t think about the global consequences of my LinkedIn connection. However, my actions, along with many others, ultimately lead to predictable, rather than random, results about how the network will evolve.

My colleagues and I used network science to study Human trafficking in the United KingdomThe noise structure In the outputs of artificial intelligence systems, and Financial corruption In the Panama Papers.

Groups have their own structure

Besides studying emerging properties such as Six Degrees of Kevin Bacon, researchers have also used network science to focus on problems such as community reveal. In simple terms, can a set of rules, known as an algorithm, automatically detect groups or communities within a group of people?

Today there are hundreds, if not thousands, of community detection algorithmsSome rely on advanced artificial intelligence methods. They are used for many purposes, including finding communities of interest and detecting malicious groups on social media. These algorithms encode axiomatic assumptions, such as expecting that nodes belonging to the same group are more closely related to each other than nodes belonging to different groups.

Although there is an interesting line of work, community discovery does not study the internal structure of societies. Should communities be considered as groups of nodes in networks only? And what about small but particularly influential communities, such as inner circles and crowds?

Two default combinations for influencer groups

Speaking way, you probably already have some knowledge about the structure of very small groups in social networks. The truth of the adage that “a friend of my friend is also my friend” can be tested statistically in friendship networks by counting the number of triangles in the network and determining if that number is higher than what chance alone can tell. Indeed, many social network studies have been used Check Claim.

Unfortunately, the concept begins to fall apart when it extends to groups of more than three members. Although the shapes are well studied in both algorithms computer science And the biologywere not reliably linked to influential groups in real communication networks.

Six groups of four points each with different configurations of the lines connecting the points
Six examples of decorations with four knots.
Mayank KejriwalAnd the CC BY-ND

Building on this tradition, my PhD student Ki Shin and found and Foot Two structures that look detailed But it turned out to be quite common in real networks.

The first structure of the triangle is extended, not by adding more nodes, but by adding straight triangles. Specifically, there is a central triangle surrounded by other periphery triangles. Importantly, the third person in any peripheral triangle should not be associated with the third person on the central triangle, thus excluding them from the true inner circle of influence.

The second structure is similar but assumes that there is no central triangle, and the inner circle is just a pair of nodes. A real-life example might be two co-founders of a startup like Sergey Brin and Larry Page of Google, or a powerful couple with common interests, common in world politicsLike Bill and Hillary Clinton.

Understand the influential groups in a notorious network

We tested our hypothesis on Enron Email Network, which is well-studied in network science, with nodes representing email addresses and links representing communication between those addresses. Although complex, not only were our proposed structures present in the network in greater numbers than would be expected by chance alone, but qualitative analysis showed that there is merit to claiming that they represent influential groups.

Two diagrams of overlapping groups of triangles labeled with people's names
Examples of the two structures found in the Enron network. There are more such structures in the network and cannot be explained by chance alone.
Mayank KejriwalAnd the CC BY-ND

The main characters of the Enron saga are we will Documented right Now. It’s interesting that some of these characters don’t seem to have had much official influence but they may have been soft power. An example is Sherry Reinarts-Cera, who was the longtime administrative assistant to Jeffrey K. Skilling, former CEO of Enron. Unlike Skilling, Sera is only mentioned in a New York Times article After the investigative investigations that took place during the scandal. However, our algorithm did detect an influential group that occupies a central position with Sera.

Anatomy of power dynamics

Society has complex structures at the level of individuals, friendships, and societies. Crowds aren’t just scattered groups of characters talking to each other, or a single gang leader calling all the shots. Many influential crowds or groups have a well-developed structure.

While there is still much to be discovered about these groups and their influence, network science can help reveal their complexity.Conversation

About the author:

Mayank KejriwalResearch assistant professor in industrial and systems engineering, University of Southern California

This article has been republished from Conversation Under a Creative Commons License. Read the original article.

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