Digital Cities: Principles of Social Networking Theory

This is the second post in a series of excerpts from my graduate research at Cornell University; each has been adapted for the purposes of this format. To read the full report, in all its technical glory, please visit my website.

Previous Topic: An Introduction to a New Civic Layer

The study of social networking theory has found refuge in many schools of thought, nowhere more so than the computer science departments of the world’s elite technical universities. Urbanists can leverage their vast computational understanding of multi-nodal social network lifecycles to construct a framework in understanding how, why, and when to deploy digital initiatives for the populace. Later we’ll combine this framework with the Roman concept of civitas to digitally enhance how citizens and the city interact within the built environment — a process known as “hybrid placemaking” (Bilandzic, Mark, and Daniel Johnson 2013).

Social networking has received an astonishing amount of attention and funding in recent years with the explosion of social media applications that create digital communities based on one’s virtual social presence. In this case, the “social” descriptor is meant to elide the complex, layered interdependencies essential to keeping a social network alive. This is key, as a social network is nothing more than a space of social appearance, a virtual realm that envelops our everyday lives, expanding and contracting as minute, real-time user interactions yield actions of collective accumulation.

“…a school of fish responds to the subtlest social cues from their surroundings in determining a collective path forward.” — from my last post

Every social network is built on the node-edge (user-relationship) model, wherein three distinct social pressures act upon each node’s relation to the other: balance, exchange, and betweenness. Acting upon the network, these social pressures create a universal law — social networks are in a constant state of rapid decay. To understand how these pressures impact a network, imagine three nodes (A, B, and C), each with the potential to be connected via a social tie to form the relationship edges of the social network.

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Our base social network will is formed by the interaction of three nodes (A, B, and C); with the potential to form a social tie, but has not yet done so — therefore, this network isn’t quite “social” yet.

Social Network Principles

Let’s take the network above and begin to add simulated interaction amongst the nodes to demonstrate our principle network pressures. Consider a scenario where A has established a social tie to both B and C; however, B and C have not yet formed such a tie. The absence of this connection manifests as latent friction on the nodal relationships between A-B and A-C, degrading the connection. Unfortunately, simply introducing a B-C relationship will not stabilize the network; it will slow the network’s rate of decay, but fails to secure increased interaction amongst the nodes. This latent pressure destabilizes the balance of the network.

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Balance: the resulting friction cause the balance of the network to be weakened, accelerating network decay.

Assume the same node relationship (A-B and A-C are tied, yet B-C not tied), if B was to form a social tie with C this might solicit more social interactions via B; however, the new relationship is not without consequence. The resulting release of exchange pressure has diminished the dependency of B on A for access to social information held by C. The ability to access this previously inaccessible knowledge held by B introduces an acceleration effect on the decay of the A-B tie. Unlike balance, any exchange pressure may be negated if B’s overall network activity is exponentially increased so that interaction overflow continues to strengthen the A-B tie.

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Exchange: an expression of counterbalancing the network, to mitigate decay B must increase activity to A.

We continue applying pressure to our network by revisiting the newly formed social ties between B and C. Prior to closing the relationship triangle, A served as a broker to B in the form of the described information access from C. This role instilled interaction growth between A and B, but was weakened upon formation. However, let’s now add a new node to the mix, D, and have it form a social tie with B. If D remains unconnected from A, the network decay due to the loss of A’s former brokerage role between B and C can be slowed. Our ability to manage the betweenness of the network in this way demonstrates how non-relational network growth accelerates social network longevity in the face of decay.

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Betweenness: as B seeks to manage their relationships, the disconnection heightens network imbalance.

In the physical world the presence of mass produces the law of gravity, an invisible universal force shaping the growth and dissipation of reality. If a single node-node relationship exists in a network, decay is inflicted upon the network — social decay is the gravity of a social network — and if it’s allowed to outpace growth, the result will be a mass exodus of nodes (users/citizens) from the network. We must seek the balance our networks while increasing overall node-node activity to mitigate the effects of exchange pressure.

Consider an apartment building of n residents and four known residents: Mary, Delano, Jackson, and Beth. Mary meets two neighbors, Delano and Beth, who have not yet met each other; thus establishing network imbalance and increasing exchange pressure through Mary. Before long Beth meets Jackson, the resulting expansion of her social network in the building weakens her ability to stay connected to her ties equally (Mary and Delano via brokerage). Before long Mary and Beth’s connection breaks due to a loss of interaction, the network is fragmented from the effect — before long no social network exists.

End of Excerpt

Next Topic: Scaling Information within Social Networks

With an high-level understanding of the three principles of social networks — balance, exchange, and betweenness — we can better understand the complex ways in which information travels through them. The next post will explore how packets of information transit the social network and introduce topics such as constructural theory, range, embeddedness, and cascade theory. Taken together we can begin to model the speed and distance of information travels once it begins traversing the network. Further we can examine that packets potential to go viral in the digital social network, and what that means for network spillover probability (i.e. jumping from one social network to another). Holistically, this will enable us to better understand how to forecast information dissemination in urban social networks.

I welcome your feedback; keep in mind this is only a part of a series in which we’ll fully vet the concepts proposed here. Opinions are my own.

Jon Kleinberg et al. 2011. “Maintaining Ties on Social Media Sites: The Competing Effects of Balance, Exchange, and Betweenness.” Association for the Advancement of Artificial Intelligence.

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