Slides - SNAP - Stanford University

snap.stanford.edu

Slides - SNAP - Stanford University

CS224W: Social and Information Network Analysis

Jure Leskovec Stanford University

Jure Leskovec, Stanford University

http://cs224w.stanford.edu


Based on 2 player coordination game

2 players –each chooses technology A or B

[Morris 2000]

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 2


If both v and w adopt p

behavior A, they each

get payoff a>0

If v and w adopt

behavior B, they

reach get payoff b>0

If v and w adopt the

opposite behaviors behaviors,

they each get 0

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 3


Let v have d neighbors

If fraction p of v’s neighbors g adopt p A, ,

then: v chooses A if: a∙p∙d> b∙(1‐p)∙d

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 4


Payoffs: aa=3 3, bb=2 2

Threshold:

Everyone is adopting B

b

v and w are initial adopters

a

Does the adoption spread?

p

b

q

Fact: When a node

switches from B to

A it never wants to

switch back to B.

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 5


Consider infinite graph G

(but each node has finite number of neighbors)

We say that a finite set S causes a cascade in

G with threshold q if, when S adopts A,

eventually every node adopts A

Example: Path

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 6


Tree:

Grid:

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 7


Def: The cascade threshold of a graph G is the

largest q for which some finite set S can cause a

cascade

Fact: There is no G where cascade threshold>½

Suppose such G exists: q>½, finite S causes cascade

Show contradiction: argue g that nodes

stop switching after a finite # of steps

Define “potential energy”: = |dout (S)|

Argue that it starts finite (non (non‐neg) neg)

and strictly decreases at every step

The only nodes that switch have a

strict majority of its neighbors in S

|dout (S)| strictly decreases

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 8


What prevents cascades from spreading?

Def: Cluster of density p is a set of nodes C

where each node in the set has at least p

fraction of edges in C.

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 9


Let S be an initial set of adopters of A

All nodes apply threshold q to decide

whether to switch to A

Two facts:

1) If G\S contains a cluster of density >(1 >(1‐q) q)

then S can not cause a cascade

2) If S fails to create a cascade cascade, then

there is a cluster of density >(1‐q) in G\S

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 10


Initially some nodes are active

Each edge (u,v) has weight wuv Each node i has a threshold t ti Node iactivates if t i Σ active(u) w ui

0.4

.88

.6

0

0.4 0.2

0.3

0.2

03 0.3

.5

03 0.3

0.4

0

0.4

0.2 0.3

.

.6

6

0.4 .4

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 11

0.3

.5

0.2

0.3

.4


So far:

behaviors A and B compete

Can only get utility from neighbors of same behavior:

A‐A get a, B‐B get b, A‐B get 0

Let’s add extra strategy gy “A‐B” – can choose both

AB‐A: gets a

AB‐B: gets b

AB‐AB: gets max(a, b)

Also: some cost c for the effort of maintaining

both strategies (summed over all interactions)

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 12


Payoff matrix:

A B AB

A a 0 a

B 0 b b

AB a b a+b‐c

There is cost c for adopting both A and B

Wave of adoption:

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 13


Every y node in an infinite network starts with B

Then a finite set S initially adopts A

Run the model for t=1,2,3,… , , ,

Each node selects behavior that will optimize

payoff (given what neighbors did in at time t‐1)

How will nodes switch from B to A or AB?

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 14


Path: Start with all Bs, Bs a>b (A is better)

One node switches to A –what happens?

With just AA, B: A spreads if ba ba

With A, B, AB: Does A spread?

Let a=2, a=2 b=3 b=3, c=1

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 15


Let a=5, a=5 b=3, b=3 c=1

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 16


Infinite path, path start with all Bs

Payoffs: A:a, B:1, AB:a+1‐c

What does node w in A‐w‐B do?

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 17


Infinite path, path start with all Bs

Payoffs: A:a, B:2, AB:a+2‐c

What does node w in AB‐w‐B do?

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 18


Joining the two pictures:

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 19


You manufacture default B and

new/better A comes along:

Infiltration: If you make B too

compatible then people will

take on both and then drop the

worse one (B)

Direct conquest: If A makes

itself not compatible –people

on the border must choose.

They pick the better one (A)

Buffer zone: If you choose an

optimal level then you keep a

static tti “buffer” “b ff ” between bt A and d B

10/11/2010 Jure Leskovec, Stanford CS224W: Social and Information Network Analysis, http://cs224w.stanford.edu 20

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