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???