On managing social data for enabling socially-aware applications and services

Distributed Systems Group CSE Department 1
On Managing Social Data for
Enabling Socially-Aware
Applications and Services
Paul Anderson, Nicolas Kourtellis,
Joshua Finnis and Adriana Iamnitchi

Distributed Systems Group CSE Department2
Outline
 Current Applications
 Limitations
 Problem Statement
 Solution
 Motivating Social Applications
 A Geo-Social Data Management Service
 Early Experiences with Real Social Data
 Decentralized Solution (not in paper)

Current Applications
 Social knowledge used in applications

Limitations
 Social information collected only from one
application domain
 limited knowledge
 high bootstrap costs
 Information from Online Social Networks
can be misleading
 hidden incentives to have many “friends”
 all friends equal

Problem Statement
How can we utilize the wealth of diverse
social information to enable novel classes
of social applications?

Solution
 Collect social information from multiple
sources - social sensors
 Maintain this information in a directed,
weighted, multi-edge social graph
 Offer a set of basic social inference
functions to allow rapid design of new
socially-aware applications and services

Motivating Social Applications
Silence phone when inappropriate

Create personalized emergency evacuation routes

Data placement based on social incentives

Outline
 Current Applications
 Limitations
 Problem Statement
 Solution
 Motivating Social Applications
 A Geo-Social Data Management Service
 Early Experiences with Real Social Data
 Decentralized Solution (not in paper)

GeoS Overview

Social Sensors
 Location (via GPS, GSM)
 Collocation (via BT)
 Schedule (e.g., Google calendar)
 Mobile phone activity (calls, sms)
 Online Social Network interactions
 Emails
 Personal relations (family)
 and others

Research Questions
 GeoS maintains a directed, weighted,
labeled, multi-edge social graph that
stores social information from multiple
social sensors
 Open research issues
 Tags of activities
 Weights for social sensors
 Aging of weights on edges in GeoS

Geo-Social Inference Functions
 Currently GeoS provides 5 basic social
inference functions
 More complex functions can be composed
from those provided
1. friend_test (ego, alter, , w)ɑ
2. top_friends (ego, , n)ɑ
3. neighborhood (ego, , w, radius)ɑ
4. proximity (ego, , w, radius, distance)ɑ
5. social_strength (ego, alter)

Social Strength (ego, alter)
 Quantifies the social strength between
ego and alter
 Result normalized to consider overall
user activity
 Search all paths of 2 social hops max

Neighborhood Inference Example

Social Strength Example
The normalized weight
from A to its adjacent
neighbor B (NW{AB}) is
the sum of all the weights
of the edges from A to B
(aggregating over all types
of interactions between A
and B) divided by the
largest of all the sums of
weights going from user A
to one of its neighbors (D)

Social Strength Example (cont)
The path strength from A
to C through B (PS{ABC})
is the lowest of all the NW
on that path, divided by
the length of the path.
The social strength from
user A to user C
(SocS{AC}) is the largest
path strength from A to C.

Early Experiences with Real Social Data
 Dataset Characteristics:
 104 randomly selected students from
NJIT campus
 Many commuters  sparse traces
 Typical users provided a few hours of
data per day
 Data recorded
 Bluetooth proximity between users
 Facebook friendships

 81 students appear
in both collocation
and Facebook traces
 Small world
characteristics
APL=2.50,CC=0.366
vs
APL=2.98,CC=0.094
in a random graph

 Application scenario
 Students participate in community volunteering
projects
 Alice wants to invite some of them to an upcoming
activity
 Using the NJIT data set
 Facebook friends (FB): <facebook, 0.1>
 Collocation 45 minutes (CL.45): <volunteering, 0.1>
 Collocation 90 minutes (CL.90): <volunteering, 0.2>
 A neighborhood() request finds students
 A social_strength() request quantifies the importance
of the edge between Alice and a returned student

Neighborhood Function Results

Social Strength Function Results
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1 2
SOCIAL HOPS FROM SOURCE
SOCSVALUE
CL.45 or FB CL.45
FB CL.90 or FB
CL.90 CL.45 and FB
CL.90 and FB

Discussion: Geo-Social Sensors
 Lots of challenges in building a reliable social sensor: must
determine label and weight of input
 Possible solutions for finding label:
 Mine text for keywords (emails, sms, blogs, etc)
 Reverse geo-coding to find where located and/or
collocated
 Use of a label ontology supplied by GeoS to maintain a
consistent dictionary of labels across different sensors.
 A sensor should dynamically calculate the weight of a user
interaction, considering:
 History of user social activity, by aggregating
frequency, time duration, time in-between activity
instances, etc.
 User’s interests
 “Familiar strangers” versus active social interaction

Discussion: User Privacy
 Perhaps the most important challenge
introduced by aggregated information
from multiple social sensors.
 Using and enforcing user policies on
what data can be collected and used
(particularly from mobile devices) is
necessary for adoption.
 Unfortunately, even if social data are
encrypted and well protected, personal
information can still be exposed by
aggregate or indirect measures (e.g.,
node degree in a graph).

Decentralized GeoS

Decentralized GeoS
 Store social information on distributed
nodes (DHT-based infrastructure)
 Use ACLs and PKI encryption to restrict data
access only to trusted users and services
 Experiments on PlanetLab to evaluate the:
 costs of inferences on an Internet-connected
distributed social graph
 effect of mapping information of socially-
connected users onto the same peer
 costs of socially-aware ACL maintenance

Thank you!
Questions?
nkourtel@mail.usf.edu

On managing social data for enabling socially-aware applications and services

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (17)

Similar to On managing social data for enabling socially-aware applications and services

Similar to On managing social data for enabling socially-aware applications and services (20)

Recently uploaded

Recently uploaded (20)

On managing social data for enabling socially-aware applications and services