Stelios Lelis
UAegean, FME: Special Lecture
Social Media & Social Networks
(SM&SN)
http://www.youtube.com/watch?v=6a_KF7TYKVc
social media (recap)
• Offer means for people to communicate that complements
face-to-face meeting.
• Offer ways for people to broadcast information to larger groups
• Offer ways for people to make social information persistent
• Offer ways for people to make ego-alter communication and
to see alter-alter communication
• Offer ways to form community around interests, e.g. music,
and also ‘long-tail’ interests, e.g. train spotting
• Offer ways to bridge constraints of time and place
• May also enrich same-time, same place social interaction
Social Network
• A social network is a set of people or groups of people with
some pattern of contacts or interactions between them
• Social network analysis focuses on the relations among
people, and not individual people and their attributes
• The social network is a group of people which we call nodes,
and connections between them called edges (or ties)
Node
Tie
Ties
• Different types of ties: family, friend, personal / professional, egoperceived / alter-perceived / mutual
• Directed (Flickr) / Undirected (Facebook)
• Strong & Weak ties
– Amount of time, emotional intensity, intimacy and reciprocal services
Strong
Tie
Weak
Tie
Path length & Neighbourhoods
• Path length: number of edges in the shortest path between two nodes
• k-hop neighbourhood of a node: the set of nodes that can be reached
through paths of length k (friends… and friends of friends… etc.)
1-hop
2-hop
3-hop
4-hop
It’s a small
world after all
Small-world
• Most pairs of nodes seem to be connected by a short path through
the network (Six degrees of separation)
• Average path length (L): Mean path length between nodes in the
network
• Diameter (D): Maximum path length between nodes in the network
• Small-world implies that spread of information will be fast
L
D
Flickr
5.67
27
Live Journal
5.88
20
Orkut
4.25
9
YouTube
5.10
21
Clustering
• Friends of friends are likely to be friends
• Clustering coefficient, C (0  C  1)
– Density of triangles in the network
– Density of links that exist between one’s friends
C
Flickr
0.313
Live Journal
0.330
Orkut
0.171
YouTube
0.136
Degree distribution
• Degree of a node: the number of edges connected to a node
• Degree, out-degree & in-degree
• Most nodes have few edges while few nodes have many edges
(Scale-free, power law degree distribution)
Node
degree = 4
Flickr
Mixing patterns – Homophily & Assortativity
•
Homophily (or assortative mixing): The tendency of people to associate
and connect with similar others
– Mixing by lines of interest, occupation, age, race, etc.
•
Assortativity: The likelihood of nodes to connect to other nodes with
similar degrees (high degree to high degree, forming a core)
•
Social networks are assortative
•
Important for the flow of information
s
r
Flickr
0.49
0.20
Live Journal
0.34
0.18
Orkut
0.36
0.07
YouTube
0.19
-0.03
Community structure
• Groups of people in the network that have a high density of connections
within them and a lower density of connections between them
Friendship
network of
children in a
US school
Structural holes
• The weaker connections between groups
– A structural hole between two groups does not mean that people in
the groups are unaware of one another.
– It only means that people are focused on their own activities such
that they do not attend to the activities of people in the other group.
Information Propagation in the
Flickr Social Network
1. Comments
2. Notes
3. Favourites
4. People
5. Tags
•
Connect to friends
•
Join groups
Information propagation & Data Collection
• Information propagation: photos’ favourite-marking
– Friends: users in the contact list of a user
– Fans: users who include a photo in their favourite photos
• Data Collection
– Crawl of the social network graph once per day for 104 days
– Each user’s favorite photos
– Timestamp when each photo was favourite-marked
Local vs. Global Picture Popularity
• Different pictures are popular among the different social network
regions;
– compare global and local
hotlists (top 100 pictures)
– no overlap between
1-hop and global
– overlap increases as
neig/hoods get wider
– 4-hop neig/hood covers
36% of entire graph
(small-world)
Distance from fans to uploaders
• Strong locality across all popularity levels
• Propagation is limited and photos rarely spread beyond the
immediate vicinity of their uploaders
Patterns of popularity growth
Active growth
Surge-increase
Sluggish
Growth evolves differently but shares common patterns
External event /
High reproduction rate
Influenced
by node
in-degree
Long-term trends in popularity growth
•
Flickr users take a long period of
time to learn about interesting
pictures
– Popular photos show an active
rise in popularity during the first
few days, and then enter a period
of steady linear growth
– Less popular photos attract their
limited fan population early on
during their lifetime and then they
become dormant
Information propagation via social links
• Social cascade: Information (or decisions/habits) spreading
through a social network one-hop at a time
• Social cascade plays a significant role in propagating
information … for both popular and unpopular pictures
Social cascades and popularity
• Social cascades play an important role in picture popularity
– Uploaders play a crucial role in the social cascades of less popular
pictures
– Social cascades of popular pictures spread information beyond the
immediate vicinity of the uploader
Peer pressure
• The probability of a user becoming a fan of a photo increases
with the number of friends who are already fans of the photo
The power of social networks
• Information does spread through social connections
• Behaviours, habits, traits & biological indices spread alike?
• The case of obesity…
http://www.nejm.org/doi/full/10.1056/NEJMsa066082
Summary
• Measures of network structure: path length, diameter, clustering,
node degree
• Properties of social networks: homophily, small-world, local
clustering, assortativity, community structure
• Information Propagation Examples
– Information propagates through social connections
– More important for popular pictures
– Most information does not spread out through the entire Flickr
network…
– …but traits do in other networks (e.g. Heart Study Obesity Network)