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Equivalence Steve Borgatti MB 874 Social Network Analysis Copyright © 2006 Steve Borgatti

equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

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Page 1: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Equivalence

Steve BorgattiMB 874 Social Network Analysis

Copyright © 2006 Steve Borgatti

Page 2: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.1

The Dream• Formalizing hallowed notions of position, role

and structure• Society as concrete network of relationships

among individuals – And social structure is underlying network of positions

structuring observed pattern among individuals• Role freed from essentialist and culturalist

definitions and defined in terms of characteristic relations among incumbents of positions, often reciprocally defined– Like functional role of species in ecosystem

Page 3: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.2

Positional Perspective

• Centrality measures one aspect of position– Unlike cohesive perspective, we class leaders

with leaders, followers with followers, regardless of who they are tied to

• But there are other aspects– Not necessarily identified, nor summarizable

in non-relational form

Page 4: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.3

Experimental Exchange Nets

• Divvy up 24 points• Who has what kinds

of outcomes?

a b c

a b c d e

f

g

Page 5: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.4

Implicit Hypothesis

• Similar nodes have similar outcomes– Occupy same position, then same results

• (Networks with similar structures will also have similar outcomes)– Similarly structured teams will have similar

performance outcomes

Page 6: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.5

Emergence

• If we can define roles formally based on observed relations, we can detect emergent, unnamed roles in groups

Page 7: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.6

Cohesion vs Equivalence

• Connectionist vs structuralist approach

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Page 8: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.7

A Collection of Concepts

• Structural equivalence• Automorphic equivalence• Maximal regular equivalence• Notes

– Lattice of regular equivalences– Equivalences versus colorations (partitions)

Page 9: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.8

Agenda

• Three equivalence concepts from theoretical point of view

• Computation and implementation

Page 10: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Structural Equivalence

Page 11: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.10

Colorations• A coloration C is just a partition of nodes.

– Assignment of nodes to exhaustive, mutually exclusive classes

– The color of a node v, written C(v) is just the equivalence class it belongs to

• An equivalence is just the relation E induced by a partition

• Is any relation that satisfies 3 conditions:– Transitivity: (a,b), (b,c) ∈ E implies (a,c) ∈ E – Symmetricity: (a,b) ∈ E iff (b,a) ∈ E – Reflexivity: (a,a) ∈ E

Page 12: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.11

Image Graphs

• Simplified models of a network, usually with a set of rules that describe correspondence between network and model

Page 13: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.12

Structural Equivalence(simplified definition)

• u ≡ v if, for any w, whenever u w then v w, and whenever w u then w v

• C(u) = C(v) if N(u) = N(v)• C(u) = C(v) if Nout(u) = Nout(v) and Nin(u) = Nin(v)

Note: Equivalent nodes have been colored the same.

y

r

r

w

y

a b

c d

e

Page 14: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.13

Structural Equivalence

• Structurally indistinguishable– Same degree, centrality, belong to same

number of cliques, etc. – Only the label on the node can distinguish it

from those equiv to it.– Perfectly substitutable: same contacts,

resources• Face the same social environment

– Similar forces affecting them

Page 15: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.14

Classical Hypothesis

• Structurally equivalent nodes will have similar internal structures | attitudes | outcomes– i.e., an explanation for homogeneity

Page 16: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.15

Mechanisms of Homogeneity

• Structural indistinguishability in the context of structural processes– Centrality– Structural holes

• Similar responses to similar environment– adaptation

• Diffusion– Through common third parties

Page 17: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.16

Pros and Cons of SE• Pros

– Captures notions like niche– Location or position

• You are your friends

• Cons– Confounds similarity with contiguity– Not helpful for explaining results of

exchange experiments– Not a good formalization of social role

• Mother & father play same role to their kids,but not other parents

• Can’t use in disconnected graphs

a b c d e

f

g

b

r

b

pr

a b

ec d

gf

Page 18: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.17

Technicality

• Definition “fails” when structurally equivalent nodes are tied to each other

• C(u) = C(v) if N(u)-{v} = N(v)-{u} is better**Even better definition is available but is more advanced

y

r

r

w

y

a b

c d

e

Page 19: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Automorphic Equivalence

Page 20: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.19

Isomorphisms

1 23

4

5

a

b

c

d e

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y

x

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Fig 3Fig 2Fig 1

Mappings:

A mapping p from one graph to another is an isomorphism ifwhenever u is tied to v, p(u) is tied to p(v).

Isomorphisms are mappings that preserve structure

Page 21: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.20

Automorphism

a b

cd

ef

An isomorphism from one graph to the same graph is an automorphism

EFCDAB

P(G)

AfBeDdCcEbFa

P’(G)G

Automorphisms constitutethe “symmetries” of a graph.

Page 22: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.21

Cycle Notation

• (1 3) (2 4) (5)

• (a b d) (c)

1 2

43

5

5524134231

p(v)v

a b

cd adccdbba

p(v)v

Page 23: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.22

Automorphic Equivalence

• Node u is automorphically equivalent to node v if there exists an automorphism p such that u = p(v)

a b

c d

e fg h

i j

Page 24: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.23

Advantages of AE• Powerful, fundamental

intuitive concept• Truly structural/positional,

not confounded by contiguity

• Captures results of exchange experiments

• Captures essentials of the role concept

• Generalization of structural equivalence that works with disconnected graphs

a b c d e

f

g

b

r

b

r

a b

fe

b

r

b

r

c d

hg

Page 25: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.24

Problems with

Automorphic Equivalence

• A parent with 2 children does not play the same role as one with 3 children

• Extremely difficult to compute

• No obvious way to relax the concept for application to real world data– No two nodes are ever AE

b

r

b

p r

gsy y

a b

ecd

f g h i

Page 26: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.25

Weak Structural Equivalence

• A coloration C of G(V,E) is weakly structural if C(u)=C(v) iff the permutation p=(u v) is an automorphism of G

a b

c d

e fg h

i j

Page 27: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

(maximal) Regular Equivalence

Page 28: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.27

The Dream• Formalizing hallowed notions of position, role

and structure• Society as concrete network of relationships

among individuals – And social structure is underlying network of positions

structuring observed pattern among individuals• Role freed from essentialist and culturalist

definitions and defined in terms of characteristic relations among incumbents of positions, often reciprocally defined– Like functional role of species in ecosystem

Page 29: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.28

Regular Equivalence• Two nodes u and v are regularly

equivalent if– Whenever u c, there exists a node

d such that v d and c and d are regularly equivalent, and

– Whenever c u, there exists a node d such that d v and c and d are regularly equivalent

• C(u)=C(v) implies C(N(u)) = C(N(v))

• Actually, C(u)=C(v) implies C(Nout(u)) = C(Nout(v)) and C(Nin(u)) = C(Nin(v))

a b

ec d

f g h i

Regularly equivalent nodes are not necessarily connected to the same third parties, but they are connected to equivalent third parties (though not necessarily in the same quantity)

Page 30: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.29

Regular Equivalence

b

r

b

rr

a b

ec d

bf

y

r

r

r

y

a b

c d

y

r

r

r

y

ab

cd

Page 31: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.30

Regular Equivalence• Captures role concept really well

– Two actors are equivalent if they have the same relations with equivalent others

– You call American airlines and talk to clerk about booking flight, while I call USAIR and do same with their clerk

• You and I equivalent because the clerks are equivalent (and they are equivalent because you and I are equivalent)

• Less strict than automorphiic– Not concerned with quantities of colors– Finds more equivalent nodes

Page 32: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.31

Regular Equivalence

• Also captures position in hierarchies well– Including trophic group

• Relatively easy to compute (and to relax)• Easy to generalize to 2-mode data

– Consumers & brands• Segments & positions• identifying category boundaries

• Works well with multiple relations

Page 33: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.32

Hierarchical Position

b

r

b

p r

gsy y

w

a b

ecd

f g h i

j

Page 34: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.33

Problems with Regular Equivalence

• Often hard to interpret– Humans good at understanding pattern

similarities, but not in the context of social ties– Data sets inappropriate for R.E. analysis

• Too small, no real roles

• A graph may have multiple colorations that are regular – especially undirected graphs

Page 35: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.34

a b

c d

e fg h

i j

a b

c d

e fg h

i j

a b

c d

e fg h

i j

Page 36: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.35

A Family of Regular Equivs

• Every structural equivalence is also regular

• Automorphic is also regular*• Actually form a lattice• Somewhat like hierarchical clustering

– Different levels of resolution

*At least as defined in this presentation. See JMS paper in 1994 for details.

Page 37: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.36

Computation

• Relaxing concepts for real world data• Two approaches

– Discrete or blockmodel• Partition nodes into mutually exclusive classes

such that departures from equivalence model are minimized

– Profile similarity• For each pair of nodes, calculate the degree to

which each pair is equivalent

Page 38: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.37

Structural Equivalence

• Profile similarity method– Compute similarity/distance between rows of

adjacency matrix• Correlation• Euclidean distance

– Much argument over handling of diagonals– Can then MDS or cluster the resulting

proximity matrix

Page 39: equivalence - Analytic Tech · Problems with Regular Equivalence • Often hard to interpret – Humans good at understanding pattern similarities, but not in the context of social

Copyright © 2006 Steve Borgatti.38

Structural Equivalence

• Blockmodeling approach– Optimization method– Older Concor method

• Actually based on profile method