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