Connected – major concepts
I am still fighting my way out of the post-pregnancy fog, but the synapses are definitely firing again. One of the really exciting benefits of that (besides the obvious fact of feeling more like my old self again), is that I want (and am finally able!) to read and write again.
I’ve been fascinated with social network theory for a long time, and I have been looking for ways to understand it better so that I could begin to position it’s value within the context of the User Experience work I do at SAP.
However, after reading Mark Granovetter’s Strength of Weak Ties in grad school, I struggled to find materials that would give me the broad introduction to the field that I felt I was missing. I joined the International Network for Social Network Analysis (INSNA) listserv a few years ago, but with the exception of a few postings that spoke about combining social network theory with ethnographic research, many of the questions have been about statistics and tools to support the analysis of network data. However, in September, someone posted a New York Times article about the book Connected: The Surprising Power of Our Social Networks and How They Shape Our Lives. I read the Times article, and it piqued my curiosity, so I ordered the book. In the meantime, it has been all over the press, including CNN. This was the first academic book that I’ve read since my pregnancy with Wynn, and it was a good way to ease into academic writing again; since this book was really written for a lay audience, the writing was very accessible and engaging.
My approach in this post is going to be to follow the general structure of the book – first to describe some of the theoretical and conceptual elements that inform the work, and then in a subsequent post, I will write about some of the case studies that I found the most compelling.
In social network theory, the study of individuals (e.g. psychology) and groups (e.g. anthropology and sociology) comes together. Part of what makes the work so fascinating is that each node on the network is a human being who may change at any time, thus altering both the network itself and any analysis of it.
In recent years, there have been major advances in social network analysis and theory, because computing technology has enabled the analysis and visualization of much larger datasets. Furthermore, it is now possible to analyze not just a network, but to visualize and analyze how that network changes over time. While network analysis may involve very large datasets, in the end, “the specific pattern of the ties is crucial to understanding how networks function” (p 9). Visualization(s) of the network is key to analyzing it effectively. Network diagrams are organized around the principle of centrality, which is to say that more interconnected nodes (or people) appear closer to the center of the diagram. As I mentioned in an earlier post, data visualization is also a topic of interest to me, so I very much appreciated the diagrams at right, which show and describe four different ways that 100 people can be in relationship with one another.
Also of interest was this representation of a real network:
Looking at the indicators for individuals A, B, C, and D helps to show the wide variation in where people may be in a network, which in turn affects how the are impacted by activity in the network. What is important, however, is not just the connection (the pattern of ties), but also contagion (what travels across the ties). The plate above shows the different types of connections and how they change the topology of the network. These elements are both critical to the analysis of the network, because people that are more connected are also more susceptible to what travels through the network.
The authors go on to describe the fundamental rules of networks:
1. We shape our networks. We determine (a) how many people are in our network (b) how interconnected those people are, and therefore (c) how central we are. These factors create a huge variety of network shapes and the placement of people within them. Furthermore, there are socioeconomic implications, for example, “those with a college degree have core networks that are nearly twice as large as those who did not finish high school”. (p 18)
2. Our network shapes us. When people that know you are better connected, you become more central – which in turn affects everything from money to happiness.
3. Our friends affect us. What flows across the connections is critical in shaping how we behave and who we become.
4. Our friends friends friends affect us. For me, this was the most surprising but most important message from the book. Hyperdydactic spread describes how something moves from person to person beyond direct social ties. In order to really understand this, whole networks need to be seen and the data must be dynamic (more than one point in time). This enables an understanding what is being transmitted and the impact. There will be more on the Three Degrees of Influence rule below.
5. The network has a life of it’s own – the sum is greater than parts.
Many people have heard the concept of Six Degrees of Separation, which has been proven many times, but we don’t necessarily have influence that broadly. Therefore the authors of Connected introduce the concept of Three Degrees of Influence, which is central to all the arguments and case studies in the book. Why three degrees and not more or less? The authors believe there are three main factors (pp 28-29):
- Intrinsic-decay explanation – like the game of telephone, or the ripple of a stone thrown into a pond, the connections deteriorate as they move away from the center
- Network-instability explanation – ties don’t last forever, and at four degrees they become unstable, leaving maybe only one path between the two people
- Evolutionary-purpose explanation – it may be just a fact of our genetic makeup / biology.
Please read about some of the Connected case studies that I found most compelling in a subsequent blog post …