Social Ties and Information Diffusion
Kristina Lerman
University of Southern California
CS 599: Social Media Analysis
University of Southern California
1
Strength of social ties and information diffusion
link = social tie
Not all ties are created equal
How do micro-level
structure (e.g., strength
of social ties) affect
macro-level
phenomena (e.g.,
information diffusion)
Strength of social ties: three views
• Theoretical analysis
– Granovetter, M. (1973) “The Strong of Weak Ties”
• Empirical analysis (mobile phone data)
– Onnela et al (2007) “Structure and ties strength in mobile
communication networks”
• Experimental study (Facebook)
– Bakshy et al (2012) “The Role of Social Networks in
Information Diffusion”
Social ties
• Strength of a tie between two people is characterized by
– Frequency and length of interactions
– Emotional intensity of interactions
– Intimacy, …
• Strong ties vs weak ties
Strong ties
• Friends
• Family
Weak ties
• Acquaintances
• Co-workers
• Neighbors
Measuring tie strength
• Intensity and duration of social interactions must be reflected
in network structure
• Consider nodes A and B
– Set S={C, D, E, …} of all nodes tied to either or both A & B
• Hypothesis: the stronger the tie between A & B, the larger the
number of nodes in S to whom both A & B are tied
C
D
E
F
C
A
A
D
B
B
E
F
Measuring tie strength
• Strong ties: large overlap in friendship circles
– These people spend much time together
• Weak ties: small overlap in friendship circles
– These people occasionally spend time together
• No ties: no overlap in friendship circles
C
D
E
F
C
A
A
D
B
B
E
F
A “bridge”
• Bridge
– a line in a network which provides the only path between
two nodes, e.g., A-B
– Link A-B provides the only route for information and
influence to flow between communities
• All bridges are weak ties
– Weak ties play an important role in diffusion
A
B
A “bridge”
• Bridge
– a line in a network which provides the only path between
two nodes, e.g., A-B
– Link A-B provides the only route for information and
influence to flow between communities
• All bridges are weak ties
– Weak ties play an important role in diffusion
A
B
Weak ties in diffusion processes
• Diffusing item can reach more nodes and traverse greater
social distance, when passing over weak ties rather than
strong ties
– If it does not pass over weak ties (bridges), diffusion will
be localized to cliques
• Surprisingly, no direct empirical evidence from sociology
literature (circa 1973), but indirect evidence
A
B
Indirect evidence from sociological studies
• In a variation of Milgram’s “small world” experiment, white
“senders” were asked to forward a letter to a black “target”
– Critical link is the first time the letter crosses from white to
black communities
– 50% of links where the white described the black as an
acquaintance (weak tie) were successful
– 26% of links where the white described the black as a
friend (strong tie) were successful
 weak ties are more effective in bridging social distance
• Study of middle school students [Rapoport & Horvath 1961]
– Students asked to nominate 1st, 2nd, etc best friend
Many more students are reached through weak ties (7th
and 8th best choices) than strong ties (1st and 2nd choices)
Job search study
• Workers find out about new jobs through personal contacts
• How does worker-contact tie strength affect chances of
learning about the new job?
– Measure tie strength by frequency of contact
• Often (at least once a week)
• Occasionally (less than 2x/week, more than 1x/year)
• Rarely (1x/year or less)
What kind of
contacts helped
workers (N=54)
find jobs?
Job search study
• Chance encounters with weak ties (college friends, former
workmates) provided most of the job leads
– “It is remarkable that people receive crucial information
form individuals whose very existence they have
forgotten”
What kind of
contacts helped
workers (N=54)
find jobs?
Length of chains
• Granovetter traced job lead back to its source, i.e., where did
the contact get information about the job from?
– direct lead from the employer  length 1
– Lead from a single intermediary  length 2
Similar to getting
job lead from ad
in newspaper
Tie strength and community organizing
• “Why do some communities organize for common goals easily
and effectively whereas others seem unable to mobilize
resources, even against dire threats?”
– In the absence of weak ties, community is partitioned in
non-interacting cliques
Cannot organize
Can organize
Summary
• Personal experience depends on large-scale aspects of social
structure
• Weak ties necessary for receiving novel information and
opportunities
• Strong ties lead to social cohesion, but overall fragmentation
• Open questions
– What about tie content, not just strength?
– How to handle negative ties?
– More empirical evidence?
Structure and Tie
Strengths in Mobile
Communication Networks
J.P. Onnela, J. Saramaki, J. Hyvonen, G. Szabo, D. Lazer, K.
Kaski, J. Kertesz,
and A.L. Barabasi
Presented by Arul Samuel Rajkumar
Focus of the Paper
• Trends in Mobile Communication Networks
– Mobile Calls
• Understand the Tie Strengths
– Weak and Strong Ties
• Understand the Network topology in Mobiles
– Community structure and its links
• Role of Tie Strengths and Network Topology in
Information Diffusion
Concepts
• Tie Strengths
weights on the edges /
links
Assumption: they affect the
information diffusion rate.
• Weights are Call duration
• Stronger Tie - more
information is shared.
Concepts
• Network Topology
Structure of the network –
Position of strong ties and
weak ties in the network.
Weak ties are bridges
between communities
• Information Diffusion
Rate
The amount of information
flowing through the links at
a specified duration.
Data Collection
• Mobile Network Calls
18 months calls from an anonymous country
Assumption:
Calls ~ Other mediums of communication in Mobile
communications
• Represented as Dyadic connections
Properties :
– One to One connection
– Intentional Sharing or spread of information between
individuals– not broadcast
• Referred to as MCG or Mobile Call Graph
Distributions of MCG
Link weight
distributio
n
Degree
Distribut
ion
• Fat tailed (skewed Power
law)
– Majority connected with ~10
people
(friends circle)
– Few people with dozens of
people
• Fat tailed
• few people talk for long time
• Majority of people talk for short
duration
Network Topology in MCG
A – communities by link strength
B – Randomly permuted link
strength
C – Link strength by betweenness
centrality
Topological Overlap
0 - no overlap
1 - complete
overlap
Network disintegration on Node removal
black - removal of ties with high weights (A) | high
<O> (B)
red - removal of ties with low weights (A) | high
<O> (B)
The spike occurs when the frequency f
become the critical frequency fc where
Real vs Control Simulated networks
How information
diffusion takes place?
• Rate of Spreading
• Community trapping
• Average weighted
ties
Who gets the
information?
• Intermediate Tie
Strength
Information Diffusion Trends
•Real Communication Networks
Inactive parts due to the weak links (slower information
transfer)
•Controlled Communication Networks
Information spreads fast due to the average weights
on the links
Conclusions
• Correlation Trends - Network topology and tie strengths
are correlated even in a dyadic network like the mobile
communication.
• Network disintegration - Removal of weak ties
disintegrate the network, but removal of strong ties only
reduce the scale of the network.
• Network topology affects the rate of information
diffusion
• Role of strong & weak ties is limited in MCF as
intermediate nodes get the information.
Limitations & New Ideas
• Mobile calls only exhibit limited communication
patterns
• Emails / Messages provides additional connections
patterns.
• Relevance of topic is also taken into consideration
for individuals to spread information.
• Relevance of Group for the individual to spread or
share information.
Key Take-aways
• Modelling network links using Tie strengths.
(weights)
• Analyzing the effect of removal of network ties.
(links)
• Controlling rate of Information diffusion by tie
strengths and network topology
Questions
The Role of Social Networks in
Information Diffusion
Eytan Bakshy, Cameron Marlow, Itamar
Rosenn, Lada Adamic
Presented by Manas Jog
Problem Introduction
Objective
1.
Identify the importance of social network in enabling
information propagation.
2.
Role of strong and weak ties in diffusion.
Challenges
Separate influence from:
Homophily
Tendency of individuals with similar
characteristics to associate with one another.
Result: Similar information sources
External correlations
External influence via email, IM or other socia
networking sites.
Result: Internal & external influences mixed!
Approach
Main idea
For each URL:
Randomly assign every user into two conditions:
No feed condition: User must not see the URL their
friends shared.
Feed condition: User can see the URL their friends
shared.
Allows us to differentiate internal influence from external
influence.
Causal relationship between Facebook Feed and external influences
can now be blocked.
Collecting Data
253,238,367
users
75,888,466
URLs
7
weeks
Temporal Clustering Analysis
Find behavior changes after an individual shares a link
Subjects share link soon after their friends share, even
when shares are hidden, but only slightly later
Effect of online contagion
Tie Strength and Influence
Measuring Tie Strength
Tie Strength is directly
proportional to the number
of interactions.
Tie Strength and Influence
Inference
1. Subject shares more when tie strength is strong.
2. Multiplicative effect of feed diminishes with tie
strength.
Similar trends for other interactions
Inferences
1. Tie strength is stronger predictor of external
influence.
2. Weak ties carry novel information that a user is
unlikely to be exposed to.
3. Weak ties increase diversity of information.
Aggregating tie impact
Assumption
One or more interactions is considered as strong tie.
Strong ties individually more
influential, but weak ties
collectively far more influential
Thank you!
Questions?
Manas Jog
Summary
• Strong ties
– surrounded by many mutual friends
– characterized by lots of shared time together
• Weak ties
– have few mutual friends
– Serve as bridges to diverse parts of the network
– Provide access to novel information