Date post: | 21-Jun-2015 |
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Technology |
Upload: | daniele-quercia |
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1. tie strength
predictors of tie strength• often: total activity• better: ?
predictors of tie strength• often: total activity• better: ?
. dynamics of phone calls for 20M users (2.5B ties)
. intensity no good to represent short-term ties
Dynamics of ties in massive communication networks
Esteban Moro Egido, Univ Madrid
predictors of tie strength• often: total activity• better: ?
. dynamics of phone calls for 20M users (2.5B ties)
. intensity no good to represent short-term ties
Dynamics of ties in massive communication networks
Esteban Moro Egido, Univ Madrid
answer: stabilityit's well-know: strong ties persistAlso see “Predicting Tie Strength with Social Media” [CHI'09]
predictors of tie strength• often: total activity• better: ?
Me: one can verify definitions of tie strength from the structure of the net. Why?
predictors of tie strength• often: total activity• better: ?
Me: one can verify definitions of tie strength from the structure of the net. Why?
We know that:1) Weak ties are bridges2) Strong ties are embeeded
2. time
• 2) extract a reasonable communication net 1) make mobility prediction
how many days of mobile data one needs to:
• 2) extract a reasonable communication net
Answer: 14 days!
1) make mobility prediction
how many days of mobile data one needs to:
• 2) extract a reasonable communication net
Answer: 14 days!
1) make mobility prediction
how many days of mobile data one needs to:
Jean Bolot, SprintMining Call and Mobility Data to Improve Paging Efficiency in Cellular Networks [Mobicom'07] We study the mobility patterns of cell phone users and develop mobility profiles...
extract global trends from an evolving graph= = segment dataset into time windows (one static graph for each window)
Jean Bolot, Sprint
Gautier Krings, UC Louvain/MIT
‘breakfast’ and then ‘dinner’,
’skipped breakfast’ and then ‘headache’ (it takes usually 6h)
‘peace’ (it’s popular on sundays)
‘happy hour’ (it’s popular not only on fridays)
‘love you’ (popular during weekends)
‘pregnant’ (on thursdays!!!)
‘exhausted’ (usually at night)
future: build a markov chain mdl to study correlations
540M tweets in USBuilt a visualization tool for temporal occurencese.eg:
Michael Macy, CornellTemporal trends of expression in twitter
3. performance & network diversity
Network Diversity and Economic DevelopmentNathan Eagle,1,2,* Michael Macy,3,4 Rob Claxton1,5
diverse personal networks are linked to strong local economy
Future: put forward whys to avoid this...
Network Diversity and Economic DevelopmentNathan Eagle,1,2,* Michael Macy,3,4 Rob Claxton1,5
“So keep building those social networks. It’s not a total waste of time. It just might be your own personal economic stimulus package.”
diverse personal networks are linked to strong local economy
There's a group of connected people solving a problem.What's the best way of connecting those people?
linear fully connected
Humans balance between exploration and exploitation
David Lazer, Harvard
There's a group of connected people solving a problem.What's the best way of connecting those people?
linear fully connected
Peak but no heterogeneity
slow
Humans balance between exploration and exploitation
David Lazer, Harvard
There's a group of connected people solving a problem.What's the best way of connecting those people?
linear fully connected
Peak but no heterogeneity
slow
Humans balance between exploration and exploitation
David Lazer, Harvard
Duncan Watts, Yahoo
Study on MechTurk. How performance is affected by net topologies and payoffs.Good news: assignments are random and controlled
Causality [science 2006]
Duncan Watts, Yahoo
Causality [PNAS 2009]
Sinan Aral, NYU Stern/MIT
Good recommender systems promote homophily and kill diversity!
Good recommender systems promote homophily and kill diversity!
Kate Erlich, IBM
Idea: connect people based on 5 types of brokerage (coordinator, gatekeeper, etc.)Inspired by “Structures of Mediation: A Formal Approach to Brokerage in Transaction Networks” '89
Me: distinguish brokers, embedded nodes, and hubs
4. Multiplex networks
face-to-face contacts
Sandy Pentland, MIT
“friends and family”
100 phones to mit members in a “residence”
surveys at different times– monthly, weekly, and asynchronously
study the subnetworks of those people (those whose religion is A, those living in floor B, those who have hobby C)
face-to-face contacts
Sandy Pentland, MIT
questions asked:
1) how influence (e.g., happiness) flows across those subnetworks
2) how to nudge people and and how to measure effectiveness (app store)
3) how friendship forms
4) how people react if they are able to control their personal data
JP Onnela, Harvard
JP Onnela, Harvard
2. time1. tie strength
3. performance & net diversity
4. multiplex networks