Analysis of Fusing Online and Co-presence Social Networks

Juan (Susan) Pan, Daniel Boston, and Cristian BorceaDepartment of Computer Science New Jersey Institute of Technology

Pervasive social applications

Traditional social apps Location-aware social apps

Socially-aware apps BUBBLE Rap

Use social knowledge to improve packet forwarding in delayed tolerant networks

Tribler Use social knowledge to reduce peer-to-peer

communication overhead 2

Social information collection

Declared by users Implicitly, through online social networks Explicitly, through surveys

Extracted from user online interactions Extracted from user mobility traces

Location traces Co-presence traces (e.g., using Bluetooth)

3

Social information representation Multiple social graphs (e.g., Facebook and co-

presence) Vertices -> users Edges -> social ties

Online social networks (OSN) provide relatively stable social graph Many connections are weak

▪ Example: actors have millions of “friends” Not all social contacts use OSN apps

Co-presence social network (CSN) identifies social ties grounded on real-world interactions Hard to differentiate social connections from passers-by

4

Research questions

Do OSN and CSN just reinforce each other or capture different types of social ties?

Can a fused network take advantage of the strengths of both? How can we quantify the benefits of this fusion? Can we measure the contribution of each

source network to the fused network?

5

Outline

Motivation Data collection Social graph representation Analysis of global network parameters Analysis of local network parameters Conclusions

6

Study participants

One month of CSN data and Facebook data for the same set of 104 students Volunteers Received compensation Belong to various departments at NJIT

7

Bluetooth based co-presence data

User Seen

TimeA B 1:00

B A 1:05

INTERNET A B 1:07

8

AB

B C 1:05

A C 1:07

Co-presence statistics

Max Mean Standard Dev.Meeting Duration 220 hrs 2

min1hr 16min 7hrs 34 min

Meeting Frequency

51 2.2 3.79

Facebook data

Subjects gave us permission to collect data Friends, wall writings,

comments, photo tags Online interaction is wall

writing, comment or photo tag Count number of

interactions between user pairs

Max Means Standard Dev.

Online Interactions

40 2 410

Outline

Motivation Data collection Social graph representation Analysis of global network parameters Analysis of local network parameters Conclusions

11

Weighted social graphs are more accurate

OSN: Weightonline = number of interactions CSN: Weightco-presence = 0.5 х Weightduration +

0.5 х Weightfrequency

How to make OSN and CSN weights comparable? Need weight normalization

OSN: Weightonline [1,40] CSN

▪ Weightduration = (Duration/MAXduration)*40 [1,40]▪ Weightfrequency= (Frequency/MAXfrequency )*40 [1,40]

12

How to remove edges due to passers-by in CSN? Very short and infrequent co-presence does not

indicate the presence of a social tie

CSN noise reduction

13

Find duration & frequency thresholds for adding a CSN edge Increase thresholds until Edit distance between CSN

and OSN stabilizes▪ Edit distance: number of edge additions/deletions to

transform one graph into the other▪ Keep OSN unchanged because Facebook friendship

confirmations validate social ties

Threshold selection

14

Total meeting duration thresholdα= 160 minutes per month

Total meeting frequency thresholdβ= 3 times per month

Resulting social graphs

Co-presence SocialNetwork

Online SocialNetwork

Fused Network (51 shared edges)15

Outline

Motivation Data collection Social graph representation Analysis of global network parameters

Degree, connectivity, centrality, cohesiveness Analysis of local network parameters Conclusions

16

OSN CSN Fused

Correlation (online, co-presence)= 0.202Average

degree3.17 3.77 5.96

• OSN degree follows proximately power law distribution

• CSN degree does not resemble as strong power-law distribution as OSN’s• Due to meeting with familiar strangers• Consequently, similar result observed for fused

network

Degree distribution

3 nodes are social

butterflies

Most nodes have high degree in either CSN or OSN, but not both 3 nodes have high degree in both CSN and OSN

Increased average degree means people meet different sets of contacts in the two source networks 17

Connectivity

OSN CSN Fused

Weighted

Number of edges 165 196 310 N

Size of LCC(largest connected component)

63 84 98 N

Diameter of LCC 7 8 7 N

Average length of shortest path

12.3 21.98 8.77 Y

CSN contributes 27% more edges than OSN• Compared to OSN, CSN

has 55% more connected people

• Almost all people connected in fused network• Average weighted shortest path reduced in

fused network• Stronger social connectivity: reason to leverage it in

social apps 18

OSN CSN Fused

Weighted

Average weight betweenness

49.1 90.13 94.83 Y

Average length of shortest path

12.3 21.98 8.77 Y

Average edge weight 3.02 3.64 1.95 Y

Average weighted clustercoefficient

0.156 0.122 0.157 Y

• CSN has much longer average shortest path than OSN• Hence, average betweenness is high

• In fused network, average shortest path is low, but betweenness is highest• Social centrality is improved

Betweenness centrality and cluster coefficient

• Average edge weight shows that people interact more in real life than online

• Highly socially active person online is not necessarily highly socially active in real life• Thus, smaller values in fused network

• OSN has higher cohesiveness• People become friends when

sharing common friends• OSN contributes more to

fused19

OSN CSN

Outline

Motivation Data collection Social graph representation Analysis of global network parameters Analysis of local network parameters

Node, edge, community Conclusions

20

Similarity of node degree and edge weight Calculate Euclidean distance of the degree vector (104

nodes) and shared edge weight vector (51 edges) Similarity is inverse of distance

Distance(OSN, CSN)

Distance(OSN, fused)

Distance(CSN, fused)

Weighted node degree

0.558 0.306 0.256

Node degree 0.399 0.305 0.225Edge weight 0.560 0.324 0.295

21

CSN more similar to fused network

Computation of community similarity How to quantify community similarity across

networks? Few communities are the same Better to quantify community overlapping

Compute k-clique overlapping clusters on the three networks separately

Use community overlapping matrix to compute distance between networks (inverse of similarity)

22

Community similarity

K=3

K=4

K=5

Dist(OSN, fused)

2561

142 26.5

Dist(CSN, fused)

2289

135 32.0

Fused network has larger average size community than OSN and CSN (fused=6.1, CSN=4.9, OSN=5.2)

CSN is closer to the fused network for weaker communities (k=3,4)

OSN is closer to fused network for stronger communities(k=5)

OSN contributes stronger social communities than CSN 23

Conclusions

CSN and OSN represent two different classes of social engagement

Applications may benefit from fused network that merges CSN and OSN CSN increases the fused network connectivity and

communication strength OSN strengthens the community structure and

lowers the average path length of fused network Typical example is friend-of-friend apps

24

Mobius project Decentralized two-tier

infrastructure for mobile social computing

P2P tier Collects on-line social information Manages social state Runs user-deployed services to support

mobile apps Dynamically adapts to geo-social context

▪ Energy-efficiency, scalability, reliability Mobile tier

Runs mobile applications Collects geo-social information from

phones

25

Application scenario: communitymultimedia sharing system

Thank you!Acknowledgment: NSF Grant CNS-0831753

http://www.cs.njit.edu/~borcea/mobius/

26

Related work

Kostakos[2010] The networks are very sparse Co-presence social ties are based on only one meeting Does not consider user interaction (edge weight) There is no proper noise reduction

Eagle[2009], Cranshaw[2010] Focused on using co-presence data to predict

friendship Mtibaa[2008]

Concluding that the two graphs are similar Conference over a single day These results cannot be broadened

27

Power Law distribution

Node degrees in real-world large scale social networks often follow a power law distribution

few nodes with many degrees and many others with few degrees

29