How Important Social Graphs are for DTN Routing

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Universidade Lusófona, IANLab meeting

Waldir Moreira

21/12/2010

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Agenda

Definitions

Motivation

Problems

Consequences

What can be done?

Current idea

Definitions - Social Graph

Vertices = nodes

Edges = levels of social relationship

• Family

• Friends

• Close Friends

• Familiar strangers

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Definitions – Types of Social Graph

Online

• Pure social networks: Facebook, Orkut

• With social network: LinkedIn, YouTube

Offline

• Traces, daily life

NOTE: At some point they intertwine

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Motivation – Online Social Graphs

Researchers in psychology and sociology

• Practice of inferring meaningful relationships from social network connections alone

Are social links valid indicators of real user interaction?

[Wilson et al. 2009] C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B Y. Zhao, "User Interactions in Social Networks and their Implications." 4th ACM European conference on Computer systems, 2009.

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Motivation – Offline Social Graphs

Mapping from the mobility process generating contacts to the aggregated social graph

How important contact aggregation is while determining social graph?

[Hossmann et a. 2010] T. Hossmann, T. Spyropoulos, and F. Legendre, “Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing.” IEEE INFOCOM'10, 2010.

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Problem

Social graph does not represent user interaction graph

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Problem

Majority of interactions occur only on a small subset of social links [Wilson et al. 2009]

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Problem

Social metrics capture part of mobility process [Hossmann et a. 2010]

– Node homogeneity

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Problem

Information is not fully available (Black Holes [Ye et al. 2010])

– Privacy

– Confidentiality

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Consequences

Disconnected social graph

Presence of non-useful links

Lost nodes

☹ Performance degradation of proposals ☹

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What can be done?

Build social graph

– Considering mobility process

– Adding information from virtual world

– As well as interets

– Timely fashion [Ferreira et al. 2010]

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Current idea

Combine aggregation techniques [Hossmann et a. 2010]

– Most recent (i.e., with more predictive power)

– Most frequent (i.e., a stronger social link)

– Select edges that represent regular contacts

Consider number of contacts as well as their frequency to determine the quality of edges comprised by the social graph

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References

[Wilson et al. 2009] C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B Y. Zhao, "User Interactions in Social Networks and their Implications." 4th ACM European conference on Computer systems, 2009.

[Hossmann et a. 2010] T. Hossmann, T. Spyropoulos, and F. Legendre, “Know Thy Neighbor: Towards Optimal Mapping of Contacts to Social Graphs for DTN Routing.” IEEE INFOCOM'10, 2010.

[Ye et al. 2010] S. Ye, J. Lang, and F. Wu, "Crawling online social graphs." Asia Pacific Web conference, 2010.

[Ferreira et al. 2010] A. Ferreira, A. Goldman, and J.Monteiro, "Performance evaluation of routing protocols for manets with known connectivity patterns using evolving graphs." Journal Wireless Networks, vol. 16, issue 3, 2010.

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Thanks!