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Small World Networks Fin

  1. 1. Small World Networks Jim Herman Partner Skylight Associates LLP [email_address] +1-617-834-4388
  2. 2. Outline <ul><li>The New Science of Networks </li></ul><ul><li>Small Worlds in Large Networks </li></ul><ul><li>Scale-Free Networks </li></ul><ul><li>Information Cascades </li></ul><ul><li>Implications for Business </li></ul><ul><ul><li>Design of Infrastructure </li></ul></ul><ul><ul><li>Organizational Structure </li></ul></ul><ul><ul><li>Mergers </li></ul></ul><ul><ul><li>Design of Collaboration Infrastructure </li></ul></ul><ul><ul><li>Change Management </li></ul></ul>
  3. 3. The New Science of Networks <ul><li>Network models are simple, yet effective ways to look at many social, economic, communication and organizational situations </li></ul><ul><ul><li>Objects (nodes) connected/related to each other in some fashion (links) </li></ul></ul><ul><li>Modeling real-world networks has moved ahead by leaps and bounds over the past 4 years </li></ul><ul><ul><li>A revolution led by empirical studies made possible by the Internet and the large databases now accessible through it, and by computer simulations </li></ul></ul><ul><li>Looks at systems where collective behavior emerges from the structure of the entire system – part of the study of complex systems </li></ul><ul><ul><li>Knowing the properties of nodes does not tell you about the bewildering complexity of interaction patterns among them </li></ul></ul><ul><ul><li>Critical point phenomenon – almost instantaneous state change at a predictable threshold </li></ul></ul><ul><li>Looks at networks dynamically, as systems that grow and change </li></ul><ul><li>Looks at what happens on or through these kinds of networks </li></ul><ul><li>These models exhibit very different properties than the “standard” view of random networks dating from the 1950’s </li></ul><ul><ul><li>It was possible to analyze this random model mathematically </li></ul></ul>
  4. 4. Small Worlds in Large Networks <ul><li>A wide variety of networks exhibit the structure of small densely interlinked clusters of nodes with a few links to other clusters </li></ul><ul><ul><li>Clusters form naturally in social networks: e.g., if two people share a common friend, it’s likely they are friends, too </li></ul></ul><ul><ul><ul><li>We have “groups of friends” more than individual friends </li></ul></ul></ul><ul><ul><li>But weaker links to other clusters allows for the interconnection of a very large number of endpoints </li></ul></ul><ul><ul><ul><li>This is how the Internet grew: LANs attaching to WAN hubs </li></ul></ul></ul><ul><ul><li>Networks with clusters are very different from random networks </li></ul></ul><ul><li>Only takes a few “shortcuts” to dramatically reduce the fragmentation and path length of large networks </li></ul><ul><ul><li>Six Degrees of Separation </li></ul></ul><ul><ul><li>Allows viruses and other infections to spread rather easily, but also new ideas and fads </li></ul></ul><ul><ul><ul><li>Short paths may exist, but can you find them? </li></ul></ul></ul><ul><ul><ul><li>Infections are “broadcast” searches, knowledge management is about directed searches </li></ul></ul></ul><ul><ul><ul><li>Today’s interconnected world of air travel and the Internet creates shorter paths between distant people than ever before </li></ul></ul></ul>
  5. 5. 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.
  6. 6. Social Networks <ul><li>Ideas, fads, biases, innovations, infections, etc. propagate through social networks </li></ul><ul><ul><li>People make decisions based on the actions and information of others they know or communicate with </li></ul></ul><ul><ul><li>People seek information when confronted with a decision or a problem to solve </li></ul></ul><ul><ul><li>People become infected with diseases by people they come in contact with </li></ul></ul><ul><li>Social networks exhibit small world structure </li></ul><ul><ul><li>The fact that most people participate in multiple types of affiliations creates a small world social network structure </li></ul></ul><ul><ul><ul><li>It’s not just geography any longer – especially with the Internet </li></ul></ul></ul><ul><ul><ul><li>Potentially much easier and faster for ideas to propagate </li></ul></ul></ul><ul><ul><ul><li>There is no where to hide! </li></ul></ul></ul><ul><ul><li>Claire Swire’s email forwarded by her boyfriend reached 7 million people in a week! </li></ul></ul><ul><ul><li>Understanding social networks can be helpful in getting any new idea or change accepted (or thwarted) </li></ul></ul>
  7. 7. Scale-Free Networks <ul><li>Many real-world networks also exhibit a “non-normal” distribution of node sizes </li></ul><ul><ul><li>A power law distribution </li></ul></ul><ul><ul><li>Averages don’t mean much </li></ul></ul><ul><li>A simple model creates real-looking networks </li></ul><ul><ul><li>Dynamic rather than static view of networks </li></ul></ul><ul><ul><ul><li>With growth, older nodes get more time to make links </li></ul></ul></ul><ul><ul><li>Preferential Treatment: when new nodes enter a network, they tend to make links with nodes that have many links </li></ul></ul><ul><ul><ul><li>The rich get richer </li></ul></ul></ul>In a Random Network, most nodes have the same number of links Number of Links (k) Number of Nodes with k Links Number of Links (k) Number of Nodes with k Links A few nodes with many links (hubs) Many nodes with only few links
  8. 8. Preferential Treatment <ul><li>It’s easy to see how social networks create preferential treatment all the time </li></ul><ul><ul><li>People make decisions based largely on how they see other people deciding </li></ul></ul><ul><li>The classic adoption curve segregates people into how willing they are to try something as a function of how many others have tried it </li></ul><ul><li>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 </li></ul><ul><ul><li>You must win over people who look to others more </li></ul></ul><ul><ul><li>Who could your early adopters influence disproportionately? </li></ul></ul><ul><ul><li>Otherwise, you’re just one more idea fighting for acceptance </li></ul></ul>T
  9. 9. Scale-Free Networks and Inequality <ul><li>Situations that lead to scale-free networks also lead to inequalities </li></ul><ul><ul><li>One node is typically twice as connected as any other </li></ul></ul><ul><ul><li>The more choices, the greater the disparity in which are adopted </li></ul></ul><ul><ul><li>Newer ideas find it hard to displace older ones </li></ul></ul><ul><ul><ul><li>But it can happen – look at Google – they have to be really good </li></ul></ul></ul><ul><ul><li>Winner could take all if compatibility with others is key </li></ul></ul><ul><li>In these kinds of networks, once an innovation takes hold, it gains a momentum that is hard to stop </li></ul><ul><ul><li>People succumb just because so many others have </li></ul></ul><ul><ul><li>Many situations where people make decisions based on their perception of how many people already have made the same decision (decision externalities) </li></ul></ul><ul><ul><ul><li>Trust as substitute for information </li></ul></ul></ul><ul><ul><ul><li>Increasing returns of things like technology </li></ul></ul></ul><ul><ul><ul><li>Issues of commons: you will cooperate if enough others will </li></ul></ul></ul><ul><ul><li>Affected by how good the innovation is, people’s threshold of acceptance, and the structure of the social network </li></ul></ul><ul><ul><ul><li>New ideas are hard to introduce in many areas of the IT industry now: acceptance of new ideas is very low </li></ul></ul></ul>
  10. 10. Information Cascades <ul><li>A recognizable aspect of real-world networks is their general stability but rare susceptibility to sudden global state changes </li></ul><ul><ul><li>Fads, epidemics, market bubbles and busts, tipping points </li></ul></ul><ul><li>Taking models from the physics of phase transitions, scientists have shown how social networks can experience these rapid changes </li></ul><ul><ul><li>Percolation theory, critical points, thresholds, non-linear math </li></ul></ul><ul><ul><li>A cascade is sparked by accessing a large, connected, “vulnerable” subnet </li></ul></ul><ul><ul><ul><li>But this is rare, which is why systems are stable most of the time </li></ul></ul></ul><ul><ul><ul><li>Many good ideas don’t fall on fertile ground – the right cluster of early adopters </li></ul></ul></ul><ul><ul><ul><li>It is still impossible to locate the right path a priori </li></ul></ul></ul><ul><ul><li>Connectivity is all that matters with a disease; local reinforcement is also needed to propagate innovations </li></ul></ul><ul><ul><ul><li>Too much connectivity can prevent cascades: hubs will not generally change their views because of one neighbor </li></ul></ul></ul><ul><ul><ul><li>It’s not the early adopters who are hard to find, but the susceptible next tier </li></ul></ul></ul><ul><li>Viral marketing tries to tap into this </li></ul><ul><ul><li>Hotmail – low threshold of adoption and a natural way to use the social network </li></ul></ul>
  11. 11. Vulnerability from Small World Nets <ul><li>In a small world network, even a weak pathogen can propagate and stay alive for a long time </li></ul><ul><ul><li>The average life of software viruses is 6 to 14 months </li></ul></ul><ul><li>The totally interconnected world of software we live in now creates serious vulnerabilities to attacks and global failures </li></ul><ul><ul><li>Diversity can be a defense; homogeneity can be dangerous </li></ul></ul><ul><ul><li>Distance can be deceiving – what seems far away could hurt your local environment suddenly </li></ul></ul><ul><li>The small world structure of today’s social networks increases the possibility of big epidemics of human disease, too </li></ul>Fraction of Random Shortcuts Epidemic Threshold of Infectiousness
  12. 12. Adaptability <ul><li>What makes a system adaptable to change and failures? </li></ul><ul><ul><li>Aisin Seiki failure: the only plant of the only producer of a critical component in Toyota’s supply chain burns down </li></ul></ul><ul><ul><ul><li>The Toyota ecosystem responded within 3 days to recreate the capability; back to full production in a week – how? </li></ul></ul></ul><ul><ul><ul><li>They were all trained in problem solving and cooperation </li></ul></ul></ul><ul><li>Interactions among problem solvers define a robust organization </li></ul><ul><ul><li>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 </li></ul></ul><ul><ul><li>Firms must support distributed communication and ad hoc team formation to remain flexible </li></ul></ul><ul><ul><ul><li>Balance between being too isolated and too connected </li></ul></ul></ul><ul><ul><li>Failure recovery requires maintenance of some form of communication </li></ul></ul><ul><ul><ul><li>A network structure that doesn’t have highly vulnerable nodes </li></ul></ul></ul><ul><ul><ul><li>Multi-scale networks – hard to fragment </li></ul></ul></ul><ul><ul><li>Networks (social, communication, etc.) can be both the source of adaptability and recovery </li></ul></ul>
  13. 13. Implications for Business <ul><li>Design of Infrastructure </li></ul><ul><li>Effective Architecture </li></ul><ul><li>Organizational Structures </li></ul><ul><li>Mergers </li></ul><ul><li>Change Management </li></ul>
  14. 14. Design of Infrastructure <ul><li>Scale-free networks are extremely robust in the face of random failures but totally vulnerable to directed attack </li></ul><ul><ul><li>DOS, virus attacks </li></ul></ul><ul><ul><li>Small problems in highly connected hubs tend to be amplified throughout the system – e.g., the airline hub system </li></ul></ul><ul><li>Complex networks can suffer from cascading failures </li></ul><ul><ul><li>Chance of failure increases if something else fails </li></ul></ul><ul><li>Move away from purely scale-free but keep the small world structure </li></ul><ul><ul><li>We may need to build more distributed networks with lots of meshing if we want more resilient systems </li></ul></ul><ul><ul><ul><li>Multi-scale networks not just hierarchical ones </li></ul></ul></ul>Multi-Scale More densely interconnected Core and Periphery Coordination mostly distant Coordination mostly Local Teams at All Levels
  15. 15. Organizational Structures <ul><li>Connectedness creates uncertainty </li></ul><ul><ul><li>Globally altering events can occur at any time </li></ul></ul><ul><li>Dealing with uncertainty requires flexibility </li></ul><ul><ul><li>Rigid hierarchies are notoriously bad in uncertain and changing times </li></ul></ul><ul><ul><li>Adaptable organizations are ones in which teams can self organize when needed to solve small and large problems </li></ul></ul><ul><ul><ul><li>Find short paths to the right experts or resources </li></ul></ul></ul><ul><ul><ul><li>Really hard problems are solved by the system not individuals </li></ul></ul></ul><ul><li>There is a balance between coordination and production </li></ul><ul><ul><li>If everyone is just going to meetings nothing happens </li></ul></ul><ul><ul><li>If no one talks to anyone outside their own local cluster, the organization is extremely fragile and resistant to change </li></ul></ul><ul><ul><li>Find ways for heads down experts to be connected via bridges </li></ul></ul><ul><li>Multi-scale networks create the most flexible organization </li></ul><ul><ul><li>Shortcuts across the traditional hierarchy </li></ul></ul><ul><ul><li>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 </li></ul></ul><ul><li>Webs of external relationships also confer robustness if done right </li></ul>
  16. 16. Mergers <ul><li>Valdis Krebs has studied how the social network in an organization affects communication and acceptance of new ideas </li></ul><ul><ul><li>Consider ways to map your various networks </li></ul></ul><ul><ul><ul><li>Inflow from Krebs ( ) </li></ul></ul></ul><ul><ul><ul><li>Email: sent mail in pairs is a good method </li></ul></ul></ul><ul><li>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 </li></ul><ul><ul><li>To get information across the old boundary </li></ul></ul><ul><ul><li>To understand how to influence the people in the other organization </li></ul></ul>
  17. 17. Merger Map
  18. 18. Design of Collaboration Infrastructure <ul><li>Communication and information sharing drive fast problem solving and adoption of new designs, processes, strategies, etc. </li></ul><ul><ul><li>Better tools for this can help make an organization more robust and adaptable, but there are limits to what any one person can handle </li></ul></ul><ul><li>Most use of tools will be for small, highly interactive groups </li></ul><ul><ul><li>So these types of interactions don’t need to scale </li></ul></ul><ul><ul><li>With something like Groove, define lots of small groups rather than one big group </li></ul></ul><ul><ul><ul><li>People can participate in multiple groups </li></ul></ul></ul><ul><li>Encourage/support “weak” cross-group links differently from group interactions </li></ul><ul><ul><li>Remember, many broad ties cannot also be deep </li></ul></ul><ul><ul><li>Local ties are more like conversations, distant ties are more like broadcast (but at least people will listen) </li></ul></ul><ul><li>Is the solution to KM really about harnessing social networks to solve the “Who Knows the Answer to This?” problem? </li></ul>
  19. 19. Effective Architecture <ul><li>Trying to get different workgroups to agree on a common architecture is a difficult undertaking </li></ul><ul><ul><li>Different understanding of problems, different contexts to use common standards </li></ul></ul><ul><li>Understanding and using an architecture requires work on the part of people </li></ul><ul><li>The set of interfaces that define an architecture might be modeled as a network of this sort </li></ul><ul><ul><li>How many are global versus local? </li></ul></ul><ul><ul><li>How are the decisions made? Who do you influence or seek advice from? </li></ul></ul><ul><li>Get the balance right between agreements for clusters and agreements that need to propagate globally </li></ul><ul><ul><li>Global agreements need participation from the right “hubs” </li></ul></ul><ul><ul><li>There can’t be too many global standards </li></ul></ul>
  20. 20. Change Management <ul><li>A key communication hub in most corporations used to be the smokers, who gather together outside frequently </li></ul><ul><ul><li>What replaces this fast track for spreading information? </li></ul></ul><ul><li>Causing a rapid information cascade depends on: </li></ul><ul><ul><li>Making the threshold of adoption low – ease of use, good out of the box experience, etc. </li></ul></ul><ul><ul><li>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 </li></ul></ul>
  21. 21. Two Great Books <ul><li>Linked – The New Science of Networks </li></ul><ul><ul><li>Albert-Laszlo Barabasi </li></ul></ul><ul><li>Six Degrees – The Science of a Connected Age </li></ul><ul><ul><li>Duncan J. Watts </li></ul></ul>
  22. 22. Thank You <ul><ul><li>[email_address] </li></ul></ul><ul><ul><li>+1-617-834-4388 </li></ul></ul>