In the new economy as more people get connected to a network, greater is the value of the network. They now connect primarily through social media networks where the vast majority of connections happen between consumers and, with increasingly frequency, organizations. These online social networks have changed forever human relations to the world and with brands. The opportunity for business is to make this networked space their operations center so that their brands can engage consumers at scale. People and communities networks are no longer a passive audience as in broadcast; they are active agents, critics with interactive relationships, who want their intelligence respected. They want to belong to something greater than themselves and can only be monetized according to their desire.

1. Eduardo Mace - January 2016 - rev.1.09
THE NETWORK ECONOMY
In the new economy as more people are connected to a network, greater is the
value of the network. They now connect primarily through social media networks
where the vast majority of connections happen between consumers and, with
increasingly frequency, organizations. These online social networks have
changed forever human relations to the world and with brands. The opportunity
for business is to make this networked space their operations center so that their
brands can engage consumers at scale. People and communities networks are
no longer a passive audience as in broadcast; they are active agents, critics with
interactive relationships, who want their intelligence respected. They want to
belong to something greater than themselves and can only be monetized
according to their desire.
Organizations are finding it difficult to understand and get results at scale from
online social networks. In my experience, businesses cannot pin down exactly
what happens in networks, because they are still geared to simple cause and
effect principles of the past. The reality of the new economy is not linear
anymore and its network effects are a challenge to visualize or control. An
example of this is what happens with some blog articles that though scarcely
shared, have a huge amount of readers. I can think of a recent example of an
article about Guanabara Bay in Rio and the Olympic Games, from a small Spanish
newspaper, which was shared directly from the source very few times in twitter,
facebook and linkedin, but had more than 18 million readers in 55 minutes. In a
monitoring analysis, we were able to verify that two influencers who linked early
to the published article generated the network effects.

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Metcalfe's Law
In 1981 Robert Metcalfe, inventor of Ethernet, proposed that the number of
connections in a digital network is roughly the square of the number of
participants connected to it. Metcalfe's law, as it was named in 1993, was
used along with Moore's law by new digital economy businesses, and still reigns
supreme in their premises to this day. Metcalfe launched with this law the
understanding of the so-called “network effects”, which in the 1990’s influenced
sociologists, physicists and virologists to start a new branch of academia:
Network Science.
The online social networks have great benefits by being on the internet, where
the cost of adding a node (person or machine) and connections (relationships)
can become marginal. If you look at the physical world, these same networks are
contained by the effects of the "efficiency of Paretto" – a principle whereby for
someone to win, another has to lose – that the digital world seems to minimize.

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From 1996 onwards, with scientists like Barabási, Dorogovtsev, Mendes and
others, networks science made several important discoveries: a) power law
distributions of scale-free networks explained by preferential attachment - thus
generating a mathematical model explaining the formation of long tail
distributions present in many industries, b) the phenomenon of Small World
networks and the importance of weak ties - read the next article, and c) how
social network architectures with their hidden structures determine node activity
and network performance. These and many other findings make up a robust
multidisciplinary body in the sciences and help scientists cope with network
complexities, allowing computer sciences to build social big data tools to
measure, monitor and analyze social networks.
Weak Ties
Back when Rolodexes were popular, there was a general feeling that networks
formed almost at random. People knew each other by chance connection,
exchanged contacts and maybe an important relationship would entail. The term
"networking" was almost synonymous with luck. Even though this might be a
good personal strategy, it is a very limited way of thinking about networks.
The network economy affects billions of people worldwide and is responsible in
part for the current robustness of growth in the US. The drive of the human
network economy is interpersonal connections based on affinities – the German
writer Goethe was the first to study marriage as a connection of affinities. Most
recently in 1954, the Russian mathematician, Rapoport and in 1973, the
American sociologist, Granovetter found that these networks are formed by
people and groups connected to each other by three types of bonds: strong,
weak and absent.

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“More novel information flows to individuals through weak than through strong
ties. Because our close friends tend to move in the same circles that we do, the
information they receive overlaps considerably with what we already know.
Acquaintances, by contrast, know people that we do not and, thus, receive more
novel information. This outcome arises in part because our acquaintances are
typically less similar to us than close friends, and in part because they spend less
time with us. Moving in different circles from ours, they connect us to a wider
world. They may therefore be better sources when we need to go beyond what
our own group knows, as in finding a new job or obtaining a scarce service. This
is so even though close friends may be more interested than acquaintances in
helping us; social structure can dominate motivation. This is one aspect of what I
have called “the strength of weak ties.” (Granovetter, 1973, 1983)
Human social networks are driven by small cohesive communities that are
connected to others by weak ties. In this sense, it only takes a bit of
interconnection between these groups to have a Small World network, an effect
popularized by the challenge named after the American actor Kevin Bacon,
where in a few connections you can reach anyone in the planet - or the network.

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Small World networks are very powerful because they are resilient, resistant to
attacks, transport information easily and filter out what is most important.
Essentially networks with this feature reduce distances and make local
connections scale globally. Networks of this type are organic, form naturally and
the best way to nurture them is not to inhibit them. A recent study on
competitiveness between companies in California found that a law that forbade
'non-compete' agreements improved exponentially the amount and quality of
innovations.
Network Analysis
With these researches and the discovery of Scale-free networks in 1998,
scientists concluded that networks have internal structures that define important
performance characteristics of the network itself. However it is only more
recently that the world began to understand about the relationship between
specific activities and network architectures - especially after the research on the
networks involved in the 9/11 attacks. Since then it was clear that terrorist
networks – like health, communication, and all the natural networks - have
temporal and hidden structures or architectures that determine how they
behave and perform. Several research groups in the US later proved that social
networks can be quantified, analyzed and managed.

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Above we have the six network architectures of twitter, each map representing a
major organization with different outcomes for their network. Each of the six
architectures has a typical node behavior and performs functions in a specific
way. The formation (or transformation) of these architectures carry a lot of value
for business relationships and can be adjusted over time for a certain purpose.
Therefore, if we understand how a network is formed, how the network activity
unfolds and know how the network architecture performs - and in what period it
manifests itself - we can do experiments and draw strategies to improve the
performance of this social network. That is why it is now possible to manage
social networks scientifically.
Networked ecosystems
“In the past, the primary role of managers was to increase efficiency. By
motivating and monitoring employees, honing the firm’s capital structure and
negotiating firmly with customers and suppliers, corporate executives could

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reduce costs across the value chain and achieve sustainable competitive
advantage”.
However today there are no more isolated verticals or industries, but rather
widely connected ecosystems with few global borders, as in the digital world. The
fact that many do not take into account this change cannot blind us to the fact
that these ecosystems already exist, are becoming digital and gain the
momentum of dominance through their networks. As in the chart below, for each
country we have a rate of inter-connectivity and maturity of ecosystems in their
use of content monetization, sharing, and network effects. (WEF 2015).
The dominance of digital networks as a driving force in economics - from the
physical directed connections networks with closed groups - to open shared
networks, with interactive connections changes everything. In digital
communities, the individuals become more relevant through their collectives.
The dynamics of ecosystems that use the digital social networking model support

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the connection of individuals to new communities, and between communities,
generating a frenzy of weak ties in Small World networks. All this is very effective
not only for communication, but for any recurring service, for intellectual services
and to share limited resources with network effects as do Uber and KickStarter.
Traditional organizations to compete in this new environment need to reach out
to the tools of network science, build Small World networks (Watts e Strogatz,
1998), accumulate Social Capital (Ferragina, 2010) and position themselves as
Brokers between communities, in what Professor Ronald Burt calls Structural
Holes (Burt, 1995 and 2004).
Structural Holes
In the concept of a Structural Hole, a Broker is the agent making the
interconnection of communities (clusters) that without it would have absent ties
among themselves. People who connect clusters in social structures are more
susceptible to generate innovations, have more influence, greater access to new
markets and are more likely to take advantage of trade between clusters. As part
of the business intelligence 3.0 stack, social network analysis (SNA) reveals the
power of each network, the sub-groups (or communities) and the individuals
within it.
Networks can be analyzed, monitored and influenced, as the large digital
conglomerates and VCs are already doing. Hence, their positioning as the current
owners of key ecosystems in mobile and the web, with the likes of AirBnb and
Houzz - to name only two - owning their respective markets through brokerage
of structural holes and carefully constructing relationship networks.
Both structuring and leveraging the opportunity between difficult to find market
offerings directly to consumers. A social monitoring and social network analysis
of any group will yield a great many insights with threats and opportunities in

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relation to possible network actions, and should be part of any intelligence stack.
Internal Networks
The same techniques apply to internal modeling of organizations. The American
Government, after the attacks on the twin towers, had to improve internal
efficiency, marksmanship (Iraq has weapons of mass destruction?) and improve
speed (stop terrorist threats). How to accomplish this in the current hierarchical
structure? How can there be more cohesion without centralization? The result
generated by an organizational network analysis revealed that of the three
possible models the better and more agile was the Small World network from a
central decision-making group. See table below for results – the shorter the
distance (betweeness) the better, because it means that the information from
any point of the network comes faster to decision makers:

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Network strategy
(3 models)
Distance to President
Distance
to decision makers
Original Hierarchical 1.67 2.88
Intelligence Czar 2.56 2.84
Interconnected Agencies 1.53 2.13
The new reality, of networks, presents us with the fact that competitive
advantage is no longer only about the sum of all efficiencies, but especially, the
resultant of all connections. It is in the collective intelligence of the networks that
current organizations using network science find ways to eliminate systemic
risks, cut entropies, gain reach, increase speed and knowledge. It is through the
strategic use of networks that organizations can build lasting mutually profitable
relationships in the digital space.
January, 2016
by Eduardo Mace (@edumace)
CEO 18moons and Managing Partner BRIAN Start-up Studio