Small World Networks Fin

© 2003 Skylight Associates LLP

Small World Networks

Jim HermanPartnerSkylight Associates [email protected]+1-617-834-4388

www.skylightassociates.com © 2003 Skylight Associates LLP2

Outline

• The New Science of Networks• Small Worlds in Large Networks• Scale-Free Networks• Information Cascades• Implications for Business

• Design of Infrastructure

• Organizational Structure

• Mergers

• Design of Collaboration Infrastructure

• Change Management


The New Science of Networks

• Network models are simple, yet effective ways to look at many social, economic, communication and organizational situations

• Objects (nodes) connected/related to each other in some fashion (links)

• Modeling real-world networks has moved ahead by leaps and bounds over the past 4 years

• A revolution led by empirical studies made possible by the Internet and the large databases now accessible through it, and by computer simulations

• Looks at systems where collective behavior emerges from the structure of the entire system – part of the study of complex systems

• Knowing the properties of nodes does not tell you about the bewildering complexity of interaction patterns among them

• Critical point phenomenon – almost instantaneous state change at a predictable threshold

• Looks at networks dynamically, as systems that grow and change• Looks at what happens on or through these kinds of networks• These models exhibit very different properties than the “standard” view of

random networks dating from the 1950’s• It was possible to analyze this random model mathematically


Small Worlds in Large Networks

• A wide variety of networks exhibit the structure of small densely interlinked clusters of nodes with a few links to other clusters

• Clusters form naturally in social networks: e.g., if two people share a common friend, it’s likely they are friends, too

• We have “groups of friends” more than individual friends

• But weaker links to other clusters allows for the interconnection of a very large number of endpoints

• This is how the Internet grew: LANs attaching to WAN hubs

• Networks with clusters are very different from random networks

• Only takes a few “shortcuts” to dramatically reduce the fragmentation and path length of large networks

• Six Degrees of Separation• Allows viruses and other infections to spread rather easily, but also

new ideas and fads• Short paths may exist, but can you find them?• Infections are “broadcast” searches, knowledge management is about

directed searches• Today’s interconnected world of air travel and the Internet creates shorter

paths between distant people than ever before


Small World Network Structure

A lot of variation in how the local clusters interconnect

- A balance between cost, path length and robustness

This structure found everywhere:

Cell metabolism, neural nets, the Web, the power grid, etc.


Social Networks

• Ideas, fads, biases, innovations, infections, etc. propagate through social networks

• People make decisions based on the actions and information of others they know or communicate with

• People seek information when confronted with a decision or a problem to solve

• People become infected with diseases by people they come in contact with

• Social networks exhibit small world structure• The fact that most people participate in multiple types of affiliations

creates a small world social network structure• It’s not just geography any longer – especially with the Internet• Potentially much easier and faster for ideas to propagate• There is no where to hide!

• Claire Swire’s email forwarded by her boyfriend reached 7 million people in a week!

• Understanding social networks can be helpful in getting any new idea or change accepted (or thwarted)


Scale-Free Networks

• Many real-world networks also exhibit a “non-normal” distribution of node sizes• A power law distribution

• Averages don’t mean much

• A simple model creates real-looking networks• Dynamic rather than static view of networks

• With growth, older nodes get more time to make links

• Preferential Treatment: when new nodes enter a network, they tend to make links with nodes that have many links

• The rich get richer

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A few nodes with many

links (hubs)

Many nodes with only few linksIn a Random Network,

most nodes have the same number of links


Preferential Treatment

• It’s easy to see how social networks create preferential treatment all the time• People make decisions based largely on how they see other

people deciding

• The classic adoption curve segregates people into how willing they are to try something as a function of how many others have tried it

• Crossing the chasm seen in this way is about moving from adoption by relatively limited clusters to adoption through a small world network of better connected people• You must win over people who look to others more

• Who could your early adopters influence disproportionately?

• Otherwise, you’re just one more idea fighting for acceptance

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Scale-Free Networks and Inequality

• Situations that lead to scale-free networks also lead to inequalities

• One node is typically twice as connected as any other• The more choices, the greater the disparity in which are adopted• Newer ideas find it hard to displace older ones

• But it can happen – look at Google – they have to be really good• Winner could take all if compatibility with others is key

• In these kinds of networks, once an innovation takes hold, it gains a momentum that is hard to stop

• People succumb just because so many others have• Many situations where people make decisions based on their

perception of how many people already have made the same decision (decision externalities)

• Trust as substitute for information• Increasing returns of things like technology• Issues of commons: you will cooperate if enough others will

• Affected by how good the innovation is, people’s threshold of acceptance, and the structure of the social network

• New ideas are hard to introduce in many areas of the IT industry now: acceptance of new ideas is very low


Information Cascades

• A recognizable aspect of real-world networks is their general stability but rare susceptibility to sudden global state changes

• Fads, epidemics, market bubbles and busts, tipping points

• Taking models from the physics of phase transitions, scientists have shown how social networks can experience these rapid changes

• Percolation theory, critical points, thresholds, non-linear math• A cascade is sparked by accessing a large, connected, “vulnerable” subnet

• But this is rare, which is why systems are stable most of the time• Many good ideas don’t fall on fertile ground – the right cluster of early adopters• It is still impossible to locate the right path a priori

• Connectivity is all that matters with a disease; local reinforcement is also needed to propagate innovations

• Too much connectivity can prevent cascades: hubs will not generally change their views because of one neighbor

• It’s not the early adopters who are hard to find, but the susceptible next tier

• Viral marketing tries to tap into this• Hotmail – low threshold of adoption and a natural way to use the social

network


Vulnerability from Small World Nets

• In a small world network, even a weak pathogen can propagate and stay alive for a long time• The average life of software viruses is 6 to 14 months

• The totally interconnected world of software we live in now creates serious vulnerabilities to attacks and global failures• Diversity can be a defense; homogeneity can be dangerous

• Distance can be deceiving – what seems far away could hurt your local environment suddenly

• The small world structure of today’s social networks increases the possibility of big epidemics of human disease, too

Fraction of Random Shortcuts

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Adaptability

• What makes a system adaptable to change and failures?• Aisin Seiki failure: the only plant of the only producer of a critical

component in Toyota’s supply chain burns down• The Toyota ecosystem responded within 3 days to recreate the

capability; back to full production in a week – how?• They were all trained in problem solving and cooperation

• Interactions among problem solvers define a robust organization• People spend most time in tight local teams, but they have

connections to distant parts of the organization (shortcuts) that allow them to solve any kind of problem

• Firms must support distributed communication and ad hoc team formation to remain flexible

• Balance between being too isolated and too connected

• Failure recovery requires maintenance of some form of communication

• A network structure that doesn’t have highly vulnerable nodes• Multi-scale networks – hard to fragment

• Networks (social, communication, etc.) can be both the source of adaptability and recovery


Implications for Business

• Design of Infrastructure• Effective Architecture• Organizational Structures• Mergers• Change Management


Design of Infrastructure

• Scale-free networks are extremely robust in the face of random failures but totally vulnerable to directed attack• DOS, virus attacks

• Small problems in highly connected hubs tend to be amplified throughout the system – e.g., the airline hub system

• Complex networks can suffer from cascading failures• Chance of failure increases if something else fails

• Move away from purely scale-free but keep the small world structure• We may need to build more distributed networks with lots of

meshing if we want more resilient systems• Multi-scale networks not just hierarchical ones

Multi-Scale

More densely interconnected

Core and Periphery

Coordination mostly distant

Coordination mostly Local

Teams at All Levels


Organizational Structures

• Connectedness creates uncertainty• Globally altering events can occur at any time

• Dealing with uncertainty requires flexibility• Rigid hierarchies are notoriously bad in uncertain and changing times• Adaptable organizations are ones in which teams can self organize

when needed to solve small and large problems• Find short paths to the right experts or resources• Really hard problems are solved by the system not individuals

• There is a balance between coordination and production• If everyone is just going to meetings nothing happens• If no one talks to anyone outside their own local cluster, the

organization is extremely fragile and resistant to change• Find ways for heads down experts to be connected via bridges

• Multi-scale networks create the most flexible organization• Shortcuts across the traditional hierarchy• When most people have approx the same number of links at all

scales, you get the easiest to search network: there are short paths and they can be found

• Webs of external relationships also confer robustness if done right


Mergers

• Valdis Krebs has studied how the social network in an organization affects communication and acceptance of new ideas• Consider ways to map your various networks

• Inflow from Krebs (www.orgnet.com) • Email: sent mail in pairs is a good method

• In trying to merge two companies, and begin to create a single organization, it is helpful to identify the hubs of the social networks in each organization• To get information across the old boundary

• To understand how to influence the people in the other organization


Merger Map


Design of Collaboration Infrastructure

• Communication and information sharing drive fast problem solving and adoption of new designs, processes, strategies, etc.

• Better tools for this can help make an organization more robust and adaptable, but there are limits to what any one person can handle

• Most use of tools will be for small, highly interactive groups• So these types of interactions don’t need to scale• With something like Groove, define lots of small groups rather than

one big group• People can participate in multiple groups

• Encourage/support “weak” cross-group links differently from group interactions

• Remember, many broad ties cannot also be deep• Local ties are more like conversations, distant ties are more like

broadcast (but at least people will listen)

• Is the solution to KM really about harnessing social networks to solve the “Who Knows the Answer to This?” problem?


Effective Architecture

• Trying to get different workgroups to agree on a common architecture is a difficult undertaking• Different understanding of problems, different contexts to use

common standards

• Understanding and using an architecture requires work on the part of people

• The set of interfaces that define an architecture might be modeled as a network of this sort• How many are global versus local?• How are the decisions made? Who do you influence or seek

advice from?

• Get the balance right between agreements for clusters and agreements that need to propagate globally• Global agreements need participation from the right “hubs”• There can’t be too many global standards


Change Management

• A key communication hub in most corporations used to be the smokers, who gather together outside frequently• What replaces this fast track for spreading information?

• Causing a rapid information cascade depends on:• Making the threshold of adoption low – ease of use, good out

of the box experience, etc.

• Spreading through the right social network – don’t try to spread through people who will get overwhelmed by lots of other conflicting influences on them


Two Great Books

• Linked – The New Science of Networks• Albert-Laszlo Barabasi

• Six Degrees – The Science of a Connected Age• Duncan J. Watts


Thank You

[email protected]

+1-617-834-4388

Technology

Small World Networks Fin