Stelios Lelis UAegean, FME: Special Lecture

1. Stelios LelisUAegean, FME: Special Lecture Social Media & Social Networks (SM&SN) http://www.youtube.com/watch?v=6a_KF7TYKVc

2. 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-altercommunication 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

3. 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

4. Ties • Different types of ties: family, friend, personal / professional, ego-perceived / alter-perceived / mutual • Directed (Flickr) / Undirected (Facebook) • Strong & Weak ties • Amount of time, emotional intensity, intimacy and reciprocal services Strong Tie Weak Tie

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. 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.

12. Information Propagation in the Flickr Social Network

13. Comments • Notes • Favourites • People • Tags

14. Connect to friends • Join groups

15. 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

16. 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)

17. 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

18. Patterns of popularity growth Active growth Surge-increase Sluggish

19. Growth evolves differently but shares common patterns External event / High reproduction rate Influenced by node in-degree

20. 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

21. 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

22. 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

23. 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

24. 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

25. 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)