New Chapters    Special Offers  
 Reviews  Deleted Scenes  Appearances  

Chapter 20

The Human Network: Alive and Clicking!

As part of a greater extent of work I was already performing at Cisco Systems, I was tasked with identifying digital and traditional influencers on behalf of the corporate communications team based on the company’s priority business units. It was an exciting opportunity as it provided me with a purpose to further study influence, linking behavior, as well as where and how these connections were forged and ranked. While this is part of influencer research in general, it should not go unsaid, that no databases or pre-existing lists were used in this process. We let influence stand on its own accord for it to be discovered. If it wasn’t discoverable, it wasn’t influential.

For this project, I initially turned to Mailana as I was already versed in its structure and output.

Using the methodologies promoted by the Conversation Prism and the Conversation Index and also incorporating the systems for recognizing conversational networks, we pinpointed voices of authority, by subject, and analyzed how they connected to each other through their professional activity and interaction. In the process, we were able to reveal the human network representative of blogs, social networks, forums, and micronetworks as well as corresponding inner networks that showcased tighter, yet distributed pockets of influence and collaboration.

Essentially we applied the principles associated with network theory envisioned through graph theory to humanize the landscape of influence.

Just for a bit of background, network theory4, as we applied it, represents the study of graphs that serve as a representation of relationships that are symmetrical, those symbolic of similar parts facing each other or around an axis.

To bring this human network to life, I turned to Pete Warden, creator of Mailana5, a social network analysis system. Warden was instrumental in the completion of this groundbreaking project as he adapted his visual networking platform to integrate our research data into a visual and interactive map of influencers that spanned across networks and mediums, organized by business units and the manual assembly of established relationships.

To further explore the behavior of hubs, nodes, hosts, intersections, connections, and distribution points, I contacted Stan Magniant of Linkfluence, a research firm based in Washington D.C. specializing in mapping, monitoring, and measuring trends and opinions on the social Web.

Magniant and team assembled a heat map based on its interpretation of graph theory to create a network topology that revealed the inbound/outbound link behavior and also the corresponding level of influence based on the quantity of nodes pointing back to distribution points or hubs. After feeding data into the Linkfluence system, both a network topology and hierarchy were immediately apparent and like the Mailana map, this too was alive and clicking.

Each node revealed inbound/outbound links and the interconnecting relationships and behavior they maintained. Influence and reach were suddenly evident and ready for exploration. We could view which individuals and properties consistently received the bulk of inbound links based on particular topics over time, while those linking back to primary sources also earned inbound links from peers at varying levels. Linkfluence revealed a hierarchical network structure that distinguished levels of influence and the corresponding networks or each.

Cisco’s human network and its discernible structure and order introduced a new landscape of influencers and paths of influence to individual business units. These graphs were not only visualized, they were reinforced by the data collected during the process of discovery. The data would later prove imperative in justifying the work and findings as decision makers require conclusive evidence and logical strategies to shift resources and attention in new direction. This work continues with Cisco and many other companies. As well, I continue to map trends and patterns within social networks using additional services such as intelligent stream mining service and 3D data visualization platform And my work continues to maintain a manual process of research and analysis to ensure the integrity of the results.

Visualizing Social Order

In sociology, social order attempts to analyze and explain how and why societies band and hold together6. For your edification, there are many oft-cited theories for explaining social order, however, I will focus on four here:

1.                  The role of shared values and guidelines (norms) in maintaining societal cohesion. This premise is most aligned with the work of Émile Durkheim7 and Talcott Parsons8.

2.                  Karl Marx9 argued that the imbalance in material wealth and political power are at the source of an ongoing conflict between different social classes. His work implies that there is no moral consensus and as such, social order is maintained on an unstable foundation.

3.                  In 1992, David Lockwood published Solidarity and Schism: The Problem of Disorder in Durkheimian and Marxist Sociology10. Lockwood proposed that neither Marx nor Durkheim resolved the issues associated with defining social order as each approach employed remaining categories that also prove as central logical elements of the other. According to an entry in, Durkheim’s work focused on the concept of moral classification as the key to social structure, whereas for Marx pivoted on production relations. One theory emphasizes the socially integrated structure of status, the other the socially divisive structure of class.

4.                  To further explore the discussion of social order, Dennis Wrong published The Problem of Order: What Unites and Divides Society11. In his book Wrong postulated that any emphasis on a single solution denies the complexity of human nature.

The Diffusion of Innovation Adoption Curve

In my experience, everything boiled down to focus. And the same applies to interactive media and digital community building and influence. It’s easy and natural to wish for attention from the early market majority now, as they are representative of a much larger group of influencers, customers, and peers. But focusing on them without building progressive momentum starting with innovators and early adopters will bypass the catalysts and champions who can help you reach and compel the early and late market majority holistically and authentically.

For example, in 2009, where would you have placed Twitter or Facebook in the bell curve? Where would you place them today? Neither started by appealing to the mainstream, they did so by attracting the groups of people who could benefit from their capabilities now and slowly evolved, refined, and adapted as they progressed through each segment — powered by people and capabilities that helped form the bridge to the next stage. Therefore, as we’re establishing programs, content, and engagement strategies, we must understand not only who we’re trying to reach and where, but also their level of sophistication and readiness and then adapt our methods accordingly.

Participation Inequality and the Laws of Percentages

Just to share a bit of interesting history, in 1906, Vilfredo Pareto, an Italian economist, developed a mathematical formula that divided the unequal distribution of wealth in Italy, observing that 20% of the people controlled roughly 80% of the wealth. In the late 1940s, Dr. Joseph M. Juran, a pioneer in the field of Quality Management, attributed the 80/20 Rule to Pareto, calling it, in what would later be recognized as a possible error, Pareto’s Principle. As Dr. Juran defined it, “The vital few and the trivial many,” the principle proposed that 20% of something is always responsible for 80% of the results.

Tenants of Community Building

Applying Harold Lasswell’s communication theory originally introduced in 1949, we are to determine, “who says what to whom in what channel with what effect.” As I introduced in my last book with Deirdre Breakenridge, in the social Web, we must analyze, “Who says what, in which channel, to what effect. Then ascertain who, hears what, shares what, with what intent, where, to what effect.” It was the recognition that communication now continues after the initial introduction or encounter.

Table of Contents

Leave a Reply

Amazon    Barnes & Noble     Borders

Wordpress Design by Adept Marketing Concepts