Week4
From INLS 490
Week 4
Contents |
[edit] Video
Social Networking in Plain English by Lee LeFever, CommonCraft
[edit] Week 4
- Notes
- Term project proposals due 9/19 (email or drop off in class)
- Be prepared to share your idea, perhaps find collaborators
- No office hours 9/19, move to 9/20 10-12 at WSM
- Evaluation of Barabasi text
- Proposal Review
- Harambee-net extra credit for feedback by 9/23
- ENISA Security Issues for Social Networking
- Essay Review
- Related readings
- This Week's Del.icio.us links
- WikiDashboard
- Social graph vs. Social network - your opinions?
[edit] Questions
[edit] Network Dynamics, Part II
[edit] Scale-Free Networks
- Non-random network, as compared to the Erdos-Renyi and Small World Models
- Growth is organic, with nodes coming to the network over time intervals
- Nodes demonstrate preferential attachment, meaning they naturally choose to connect with highly-connected nodes (the authorities)
- The rich-get-richer phenomenon?
[edit] Questions
- All clear on scale-free networks, preferential attachment, hubs, authorities, etc?
- Can we articulate how hubs and authorities emerge in the blogosphere, or on the web?
- Do we understand how search relevance algorithms leverage network models?
- Questions about network models?
[edit] Golder et. al.
- Messaging within FB exhibits temporal rhythms that are robust and consistend across campuses and seasons
- College student weekend - Midday Friday-Sunday
- College student week - Sun Eve - Friday
- Grouping effects/clusters at schools - there were similarities in messaging patters at schools
- Nearly all communication was found to occur between friends, and only a small proportion of friends exchanged messsages (90.6% msgs/87.5% pokes between friends)
- Compared to email, FB messages are spares .97msgs/week/user
- Are messages and pokes a satisfactory proxy for gauging and understanding social activities on a large scale? How would you improve this study?
- 15.1% of friend pairs exchanged messages - what does this say about social network friendship?
- Pokes bias heavily towards same-school - does this make sense? If so, why?
- Highest levels of reciprocity in established-friends, same-school messages, lowest reciprocity in non-friend, different-school messages.
- Facebooking and schoolwork correlation - does this make sense?
- FB use decreases during traditional periods of "social time" - what does this tell us?
- No significant shift in patterns over summer time - Facebook as friend-maintenance tool.
[edit] Kumar et. al.
- Members of a social network
- Singletons - People who have joined but not added friendships
- Giant component - people who comprise the mass of the linkages
- Middle region - Isolated communities that don't interact with the giant component
- Kumar - the likelihood that two isolated communities will merge is unexpectedly low - how do our graph models explain this?
- Isolated communities and stars
- Star - a single "charismatic" individual connecting a group
- Stars are less important thatn the giant component - they are an edge phenomenon
- How an individual joins a network
- They actively seek it out (loners and giant component)
- They are invited by a friend or colleague (stars, middle region - stars wish to migrate their offline contacts online)
- Model of biased preferential attachment says that it is easy to find connections in the giant component, but more difficult in the isolated communities.
- RQ1 - Are friendships reciprocal in online social networks?
- Flickr: 70.2%
- Yahoo 360: 84%
- Friends tend to reciprocate quickly, usually in a day [Image]
- What do high levels of reciprocity say about a network? What are levels of reciprocity lower in Flickr?
- RQ2 - How does density in social networks change over time
- 1) Early adopters join and add lots of friendships
- 2) More users join and dilute the densite of the pool
- 3) Organic growth - people narutally add friends [Image]
- RQ3 What is the dynamics of component formation and evolution in OSN's?
- Over time, the density fractions change - more singletons = weaker network?
- RQ4 How do components merge with each other as nodes and edges arrive in OSNs?
- Singletons merging with the non-giant component (stars)
- Non-giant component merging with the giant component (organic growth)
- RQ5 Do the compnents in the middle region have any special structure?
- Middle region large composed of stars - Flickr 92.8% stars, Y360 88.7% stars
- RQ6 How does the diameter of the social network behave as a function of time?
- Avg vs. Effective diameter
- Flickr - Avg 6.01, Eff 7.61
- Y360 Avg 8.26, Eff 10.47
- Diameter has high correlation with density over time
- In the third stage diameter starts shrinking [Image]
- RQ7 - Does the giant component have a reasonably small core of nodes with high connectivity?
- Giant component characterized by well-connected core at the center
- Model of networks - types of users
- Passive - Join out of curiosity, don't engage in significant activity
- Inviters - interested in migrating an offline community into the online space
- Linkers - full participants
