Are You moved by Your Social Network Application?

Abderrahmen Mtibaa, Augustin Chaintreau, Jason LeBrun, Earl Oliver,

Anna-Kaisa Pietilainen, Christophe Diot

Thomson Paris Research Lab

Avinash Patlolla

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

IntroductionBefore the Internet: socialize by

physical meetingToday: Internet allows “virtual”

socializingTomorrow: Meet the virtual

community using opportunistic contacts and locality

Motivation

Explore the relation between virtual social interactions and human physical meetings

Understand complex temporal properties based on simple social properties

Forwarding based on social network properties

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

Experimental SetupDistributed smartphones with mobile

opportunistic social networking application to 28 participants (but later reduced to 27)

3 days experiment at CoNext December ‘07Initially, each participant was asked to

identify friends among the 150 CoNext participants.

Applications:◦Opportunistic socializing: make new friends

based on friends◦Asynchronous messaging

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

Terminology and DefinitionsSocial graph: Graph of friendship between

participants. Denoted as G = (V, E)Contact graph: Collection of opportunistic

Bluetooth contacts between the participants form the temporal network , which is called contact graph. Denoted as Gt = (V, Et)

Delay- optimal path: A path, in contact graph, from s to d starting at time t0 is delay-optimal if it reaches the destination d in the earliest possible time. The delay optimal path can be computed via dynamic programming

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

Topological comparison

Initial Graph Final Graph

# nodes# edgesAverage degreeDiameter

27685.27

271299.54

Node propertiesCharacterize Node heterogeneity

◦High/low activity◦Popularity◦Contact rate

Two metrics are measured◦Node degree

Social graph: number of friends Contact graph: average number of devices seen per

scan (every 2 min.)

◦Centrality of nodes: Social graph: measure the occurrence of the node

inside all shortest paths Contact graph: measure the occurrence of the node at

each time t inside all shortest paths

Node degree

Ordering error =

Centrality of nodes

Contact propertiesCompare contact according to:

◦ Social distance (friends have distance 1, friends of friends of friends have distance 2 and so on)

◦ time between two successive contacts

Path propertiesDelay-optimal paths as a function of the social

distance between the source and the destination

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

Social forwarding pathsGeneral model: depending on the source s and the

destination d, a rule defines a subset of directed pairs of nodes (u v) so that only the contacts occurring for pairs in the subset are allowed in forwarding path.

Path construction rules:◦ neighbor(k): (u v) is allowed if and only if u and v are

within distance k in the social graph◦ destination-neighbor(k): (u v) is allowed if and only if v

is within distance k of d◦ non-decreasing-centrality: (u v) is allowed if and only if C(u) <= C(v)◦ non-increasing-distance: (u v) is allowed if and only if

the social distance from v to d is no more than the one from u to d

Performance of different path construction rule

Comparison of rules

• The neighbor rule performs reasonably well• The rule based on centrality outperforms all the other rules considered• The combination of neighbor and centrality rules reduce the cost (offers best trade-off)

OutlineIntroduction

◦MotivationExperimental setupTerminology and DefinitionsTopological comparisons

◦Node properties◦Contact properties◦Path properties

Social forwarding pathsSummary and critiques

Summary and CritiquesSimilarity in the properties of nodes,

contacts and paths in the two graphsHighlighted the importance of centrality of

nodesUsing social neighbors to communicate can

be effective to exchange messages with opportunistic bandwidth

Critiques:◦ Important issues not yet studied, like computing

centrality of nodes in a distributed manner.◦Scalability and usability◦Not many technical details

References http://www.docstoc.com/docs/5083899/Are-You-moved-by-Your-Social-

Network-Application

Questions???