The Network Revolution: Understanding Social Interactions in Modern Society

1. Seeing Networks Barry Wellman, NetLab Department of Sociology University of Toronto wellman@chass.utoronto.ca

2. The Turn to Networked Individualism • Functioning in Encompassing , Densely-Knit, Bounded Groups  • Fragmented, Sparsely-Knit , Permeable & Specialized Networks • MyFace (sic) is only the most media-hyped aspect

3. The Triple Revolution • The Internet Revolution • The Mobile (Connectivity) Revolution • The (Social) Network Revolution

4. The Internet Revolution • Builds on and Reinforces the Network Revolution • Instant Access to Diverse, Copious Information • If You Know Much to Look • Rapid, Low-Cost Communication • Distance, Time Much Less of a Constraint • Email as Frequent with Ties 3K km & 3 km • Yet most ties are local – people have bodies! • Supports Larger Networks • Increasing Volume and Velocity of Info & Comm

5. Social Affordances of New Forms of Computer-Mediated Connectivity • Bandwidth • Ubiquity – Anywhere, Anytime • Convergence – Any Media Accesses All • Portability – Especially Wireless • Globalized Connectivity • Personalization

6. Mobile Revolution • The Newest • Information & Communication Available • Wherever You Are • Wherever You Go • Always On, Always Connected • Multiple Venues of Connectivity – • Social Venues • Physical Venues – home, work, Starbucks

7. The Network Revolution • The Subject of Our Talk • Actually Came First • We Think of Groups; We Function in Networks • No longer densely-knit • Fragmented – people switch & maneuver among nets • Specialized role relationships • Social capital from boutiques & not general stores • Premium on individual agency, rather than letting the group do it • Find your own information – no more 2-step flow • Maneuver/manipulate thru your networks.

8. Traditional Ways of Looking at Social Interactions • Individuals as Aggregates of Attributes • All Possess One or More Properties as an Aggregate of Individuals • Examples: Sex, Education, Bank, Rich Countries • Groups • (Almost) All Densely-Knit Within Tight Boundary • Thought of as a Solidary Unit (Really a Special Network) • Family, Workgroup, Community, Association, Soviet Bloc

9. The Network Approach • Network • Set of Connected Units: People, Organizations, Networks • Relations: Direct relations or common affiliations • Talking, cheating, working together, trade, liking, partnership, citation, disease transmission, marriage, travel • Can Belong to Multiple Networks • Examples: Friendship, Organizational, Inter-Organizational, World-System, Internet

10. Nodes, Relationships & Ties • Nodes: A Unit That Possibly is Connected • Individuals, Households, Workgroups,Organizations, States Relationships (A Specific Type of Connection) A “Role Relationship” • Gives Emotional Support • Sends Money To • Attacks • Ties (One or More Relationships) • Friendship (with possibly many relationships) • Affiliations (Person – Organization) • Works for IBM; INSNA Member; Football Team • One-Mode, Two-Mode Networks

11. Social Network Analysis • The Analysis of Networks! Simple enough, eh? • But network analysis implies a new perspective for understanding social behavior • Not a method, a cognitive perspective that has developed methods for applying that perspective to empirical research

12. The Social Network Perspective • Relations, not attributes • No independence! • Dyadic relations operate in the context of broader social structures

13. Networks Before Network-ing • Original ideas in the early 1900s – Georg Simmel • First research in the 1930s – J.L. Moreno • Modern Era of theory/research – mid 1960s: Harrison White, etc. • International Network for Social Network Analysis founded at U of Toronto, 1976 • Email in late 1980s • Networking software (Facebook) in this decade

14. Networks, Not Groups • “Groups” are a short-hand for special kinds of networks: cohesive, densely-knit & tightly-bounded • Group = binary membership status • Network – varied levels of embeddededness, variable knit, often loosely bounded • Networks can comprehend multiple memberships & commitments, as well as conflicting interests

15. A Network is More Than The Sum of Its Ties • A Network Consists of One or More Nodes • Could be Persons, Organizations, Groups, Nations • Connected by One or More Ties • Could be One or More Relationships • That Form Distinct, Analyzable Patterns • Can Study Patterns of Relationships OR Ties • Emergent Properties (Simmel vs. Homans)

16. Relations, Not Attributes Behavior of actors is best explained by: Position of actors in patterns of relations Not the attributes of actors (sex, SES, ethnicity) Although attributes may be correlated with positions: for example, central high-status white men

17. Dyads are Influenced by Network Context In a sentence: “To Discover How A, Who is in Touch with B and C, Is Affected by the Relation Between B & C” John Barnes, British sociologist, anthropologist, 1970s

18. The Multiple Ways of Network Analysis • Method – The Most Visible Manifestation • Misleading to Confuse Appearance with Reality • Data Gathering • Theory – Pattern Matters • Substance • Community, Organizational, Inter-Organizational, Terrorist, World System, Web • As an Add-On: • Add a Few Network Measures to a Study • Integrated Approach • A Way of Looking at the World: • Theory, Data Collection, Data Analysis, Substantive Analysis • Links to Structural Analyses in Other Disciplines

19. The Social Network Approach • The world is composed of networks - not densely-knit, tightly-bounded groups • Networks provide flexible means of social organization and of thinking about social organization • Networks have emergent properties of structure and composition • Networks are a major source of social capital mobilizable in themselves and from their contents • Networks are self-shaping and reflexive • Networks scale up to networks of networks

20. How Do Network Analysts Explain Things? • Some don’t. Pure formalists discovering structure • How structure affects outcomes: • Sparsely knit networks provide a greater variety of resources • Structure as providing constraints and opportunities – manuverability of multiple clusters • Structure matters more than individual attributes • Structure helps explain individual motivations

21. Explanation by Structure Alone • Understanding of motivation not necessary to explain outcomes • Harrison White: chains of opportunity (vacancy chains) • Jobs, homes

22. Structure as Constraint & Opportunity • People pursue their goals within structure • Structure provides opportunities to pursue goals & constraints on action • e.g., Ron Burt’s Structural Holes

23. Structural vs Other Explanations • Determine how much variation is accounted for by structure and how much by other explanations • e.g., Beverly Wellman: “Pathways to Back Care” • How people find alternative health care providers

24. Structure as Source of Motivations • People “catch” peferences, goals, motivations, etc from their networks: • Epidemiology – attitudes to birth control; AIDs • Two methods: • Cohesion – from those to whey are connected • E.g., Poison Pills and Golden Parachutes • Equivalence – From those in similar network positions • Citation studies – White, Wellman & Nazer; Matzat

25. Changing Connectivity: Groups to Networks • Densely Knit > Sparsely-Knit • Impermeable (Bounded) > Permeable • Broadly-Based Solidarity > Specialized Multiple Foci

26. Characteristics of a Networked Society • Multiplicity of specialized relations • Management by networks • More alienation, more maneuverability • Loosely-coupled organizations / societies • Less centralized • The networked society

27. Little Boxes: Door-to-Door • Old Workgroups/ Communities Based on Propinquity, Kinship • Pre-Industrial Villages, Wandering Bands • All Observe and Interact with All • Deal with Only One Group • Knowledge Comes Only From Within the Group – and Stays Within the Group

28. Little Boxes GloCalization Networked Individualism BW, “From Physical Place to Cyber Place”, Intl J of Urban & Regional Research, 2001

29. Place To Place: GloCalization (Phones, Networked PCs, Airplanes, Expressways, RR, Transit) Home, Office Important Contexts, • Not Intervening Space • Ramified & Sparsely Knit: Not Local Solidarities • Not neighborhood-based • Not densely-knit with a group feeling • Partial Membership in Multiple Workgroups/ Communities • Often Based on Shared Interest • Connectivity Beyond Neighborhood, Work Site • Household to Household / Work Group to Work Group • Domestication, Feminization of Community • Deal with Multiple Groups • Knowledge Comes From Internal & External Sources • “Glocalization”: Globally Connected, Locally Invested

30. Person To Person: Networked Individualism (Cell Phones, Wireless Computing) • Little Awareness of Context • Individual, Not Household or Work Group • Personalized Networking • Tailored Media Interactions • Private Desires Replace Public Civility • Less Caring for Strangers, Fewer Weak Ties • Online Interactions Linked with Offline • Dissolution of the Internal: All Knowledge is External

31. Role To Role Tailored Communication Media • Little Awareness of Whole Person • Portfolios of Specialized Relationships • Boutiques, not Variety Stores • Cycling among Specialized • Communities / Work Groups • Role-Based Media Interactions • Management by Network

32. The “Fishbowl” Group Office:(Little Boxes) • All Work Together in Same Room • All Visible to Each Another • All have Physical Access to Each Other • All can see when a Person is Interruptible • All can see when One Person is with Another • No Real Secrets • No Secret Meetings • Anyone can Observe Conversations & Decide to Join • Little Alert to Others Approaching

33. Neighbors have Hi Visual & Aural Awareness • Limited Number of Participants • Densely-Knit (most directly connected) • Tightly Bounded (most interactions within group) • Frequent Contact • Recurrent Interactions • Long-Duration Ties • Cooperate for Clear, Collective purposes • Sense of Group Solidarity (name, collective identity) • Social Control by Supervisor & Group

34. The “Switchboard” Network Office:Networked Individualism • Each Works Separately • Office Doors Closable for Privacy • Glass in Doors Indicate Interruptibility • If Doors Locked, Must Knock If Doors Open, Request Admission • Difficult to learn if Person is Dealing with Others Unless Door is Open • Large Number of Potential Interactors • Average Person knows > 1,000 • Strangers & Friends of Friends May also be Contacted

35. Sparsely-Knit • Most Don’t Know Each Other • Or Not Aware of Mutual Contact • No Detailed Knowledge of Indirect Ties • Loosely-Bounded • Many Different People Contacted • Many Different Workplaces • Can Link with Outside Organizations • Each Functions Individually • Collective Activities Transient, Shifting Sets • Subgroups, Cleavages, Secrets Can Develop

36. Little Boxes  Ramified Networks **** Each in its PlaceMobility of People and Goods **** • United Family  Serial Marriage, Mixed Custody • Shared Community  Multiple, Partial Personal Nets • Neighborhoods  Dispersed Networks • Voluntary Organizations  Informal Leisure • Face-to-Face Computer-Mediated Communication • Public Spaces  Private Spaces • Focused Work Unit  Multiple Teams • Hierarchical Org.  Networked Organization • Job in a Company  Career in a Profession • Autarky  Outsourcing • Office, Factory  Airplane, Internet, Cellphone • Ascription  Achievement • Conglomerates  Virtual Organizations/Alliances • Cold War Blocs  Fluid, Transitory Alliances

37. Ways of Looking at Networks • Whole Networks & Personal Networks • Focus on the System or on the Set of Individuals • Graphs & Matrices • We dream in graphs • We analyze in matrices

38. Network Data • Observation • Archival • Name Generators/Interpreters • Position Generators • Resource Generators

39. What Do Network Data Look Like? • Most quantitative data = one row per unit, with variables representing unit's attributes • Network data = data about relations between units • We dream in graphs; we analyze in matrices

40. Whole Social Networks • Comprehensive Set of Role Relationships in an Entire Social System • Analyze Each Role Relationship – Can Combine • Composition: % Women; Heterogeneity; % Weak Ties • Structure: Pattern of Ties • Village, Organization, Kinship, Enclaves, World-System • Copernican Airplane View • Typical Methods: Cliques, Blocks, Centrality, Flows • Examples: (1)What is the Real Structure of an Organization? • (2) How Does Information Flow Through a Village?

41. Whole Networks vs. Ego Networks • Personal Networks = the network surrounding one person (node) • Person tied with Alters • Alters’ characteristics • Connections between alters • Normally collected for multiple Egos • Whole Networks = Network of a particular setting or population. Bird's eye view of network, not focused on one person

42. Network Graphs Whole Person

43. Costs of Whole Network Analysis • Requires a Roster of Entire Population • Requires (Imposition of) a Social Boundary • This May Assume What You Want to Find • Hard to Handle Missing Data • Needs Special Analytic Packages • Becoming Easier to Use

44. Duality of Persons & Groups • People Link Groups • Groups Link People • An Interpersonal Net is an Interorganizational Net • Ronald Breiger 1973

45. The Dualities of Persons and Groups -- Graphs

46. Network Size Matters • (Robert) Metcalfe’s Law – (Xerox PARC, 1973) • For every network member added • The number of possible ties grows by N2 • 10 people => 102 possible ties = 100 • (David) Reed’s Law (MIT emeritus, 1997) • For every network member added • The number of possible (sub)groups grows by 2N • 10 people => 210 possible groups = 1,024 • Not only does Reed give a higher number than Metcalf • The disparity increases greatly as N increases • However, many of these subgroups are very similar

47. Personal Social Networks • Ptolemaic Ego-Centered View • Good for Unbounded Networks • Often Uses Survey Research • Example: (1)Do Densely-Knit Networks Provide More Support? (structure) • (2) Do More Central People Get More Support? (network) • (2) Do Women Provide More Support? (composition) • (3) Do Face-to-Face Ties Provide More Support Than Internet Ties? (relational) • (4) Are People More Isolated Now? (ego)

48. 25.0% 20.0% 15.0% Percentage of valid cases 10.0% 5.0% 0.0% 5 15 25 35 45 55 65 # of network members Network Size: The Myopia of “Bowling Alone” 40 30 20 10 0 Very Somewhat

49. Social Network Analysis: More Flavors • Diffusion of Information (& Viruses) • Flows Through Systems • Organizational Analyses • “Real” Organization” • Knowledge Acquisition & Management • Inter-Organizational Analysis • Is There a Ruling Elite • Strategies, Deals • Networking: How People Network • As a Strategy • Unconscious Behavior • Are There Networking Personality Types?

50. Branching Out (II) • Social Movements • World-Systems Analyses • Cognitive Networks • Citation Networks • Co-Citation • Inter-Citation • Applied Networks • Terrorist Networks • Corruption Networks • Web Networks