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Jon Crowcroft Pan Hui Computer Laboratory University of Cambridge

Bubble Rap: Forwarding in Small World DTNs in Ever Decreasing Circles Part 2 - P eople A re the N etwork. Jon Crowcroft Pan Hui Computer Laboratory University of Cambridge. Outline. Multiple levels human heterogeneity Local community structures

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Jon Crowcroft Pan Hui Computer Laboratory University of Cambridge

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  1. Bubble Rap: Forwarding in Small World DTNs in Ever Decreasing CirclesPart 2 - People Are the Network Jon Crowcroft Pan Hui Computer Laboratory University of Cambridge Imperial College London

  2. Outline • Multiple levels human heterogeneity • Local community structures • Diversity of centrality in different scales • Four categories of human relationship • Heterogeneous forwarding algorithms • Design space • RANK (centrality based forwarding) • LABEL (community based forwarding) • BUBBLE RAP (centrality meets community) • Approximation and predictability • Decentralized approximation of centrality • Human predictability Imperial College London

  3. The first goal of this research is to move to a third generation of human mobility models, understanding heterogeneity at multiple levels of detail. Understanding multiple levels of heterogeneity Imperial College London

  4. Social Structures Vs Network Structures • Community structures • Social communities, i.e. affiliations • Topological cohesive groups or modules • Centralities • Social hubs, celebrities and postman • Betweenness, closeness, inference power centrality Imperial College London

  5. K-clique Community Definition • Union of k-cliques reachable through a series of adjacent k-cliques [Palla et al] • Adjacent k-cliques share k-1 nodes • Members in a community reachable through well-connected well subsets • Examples • 2-clique (connected components) • 3-clique (overlapping triangles) • Overlapping feature • Percolation threshold pc (k)= 1/[(k-1)N]^(1/(k-1)) Imperial College London

  6. K-clique Communities in Cambridge Dataset Imperial College London

  7. K-clique Communities in Infocom06 Dataset Barcelona Group Paris Groups Lausanne Group Barcelona Group Paris Group A Paris Group B Lausanne Group K=3 Imperial College London

  8. K-clique Communities in Infocom06 Dataset Barcelona Group Paris Groups Lausanne Group Barcelona Group Paris Group A Paris Group B Lausanne Group K=4 Imperial College London

  9. K-clique Communities in Infocom06 Dataset Paris Group A (French) Barcelona Group (Spanish) Italian Paris Group B (French) K=5 Imperial College London

  10. Other Community Detection Methodologies • Betweenness [Newman04] • Modularity [Newman06] • Information theory[Rosvall06] Imperial College London

  11. Centrality in Temporal Network • Large number of unlimited flooding • Uniform sourced and temporal traffic distribution • Number of times on shortest delay deliveries • Analogue to Freeman centrality [freeman] Imperial College London

  12. Homogenous Centrality Reality Cambridge Infocom06 HK Imperial College London

  13. Within Group Centrality Cambridge Dataset Group B Group A Imperial College London

  14. Within Group Centrality Reality Dataset Group A Group B Group D Group C Imperial College London

  15. Model Node Centrality Node centrality should be modelled in different levels of heterogeneity Imperial College London

  16. Regularity and Familiarity Regularity I Correlation Coefficient = 0.9026 II IV III Familiarity I: Community II. Familiar Strangers III. Strangers IV. Friends Imperial College London

  17. c: 0.5817 c: 0.6604 Infocom05 Infocom06 c: 0.8325 c: 0.7927 HK Reality Imperial College London

  18. Heterogeneous Forwarding The second goal of this research is to devise efficient forwarding algorithms for PSNs which take advantage of both a priori and learned knowledge of the structure of human mobility. Imperial College London

  19. Interaction and Forwarding • Third generation human interaction model • Categories of human contact patterns • Clique and community • Popularity/Centrality • Dual natures of mobile network • Social network • Physical network • Benchmark Forwarding strategies • Flooding, Wait, and Multiple-copy-multiple-hop (MCP) Imperial College London

  20. Design Space Explicit Social Structure Bubble Label Human Dimension Structure in Cohesive Group Clique Label Network Plane Rank, Degree Structure in Degree Imperial College London

  21. Greedy Ranking Algorithm (RANK) • Use pre-calculated centrality/rank • Push traffic to nodes have higher rank • Good performance in small and homogeneous Imperial College London

  22. Greedy Ranking Algorithm • Hierarchical organization • Hierarchical paths [Trusina et al] • High percentage in most dataset Imperial College London

  23. Problem with RANK • Heterogeneous at multiple levels • Best node for the whole system may not be best node for a specific community C D D B E A Imperial College London

  24. Problem with RANK Hop distribution and rank at dead-end for HK dataset Imperial College London

  25. Label Strategy (LABEL) • Priori label, e.g. affiliation • Correlated interaction • Forward to nodes have same label as the destination • Good performance in conference mixing environment Infocom06 Imperial College London

  26. Problem with LABEL • In a less mixing environment (e.g. Reality) • A person in one group may not meet members in another group so often • Wait for destination group not efficient Imperial College London

  27. Centrality meets Community • Population divided into communities • Node has a global and local ranking • Global popular node like a postman, or politician in a city • Local popular node like Christophe Diot in SIGCOMM • BUBBLE-A • BUBBLE-B Imperial College London

  28. Destination Ranking Subsub community Sub community Sub community Source Imperial College London Global Community

  29. Centrality meets Community Imperial College London

  30. Making Centrality Practical How can each node know its own centrality in decentralised way? How well does past centrality predict the future? Imperial College London

  31. Approximating Centrality • Total degree, per-6-hour degree • Correlation coefficients, 0.7401 and 0.9511 Imperial College London

  32. Approximating Centrality • DEGREE • S-Window • A-Window (Exponential Smoothing) Imperial College London

  33. Predictability of Human Mobility • Three sessions of Reality dataset • Two sessions using the ranking calculated from the first session • Almost same performance Imperial College London

  34. Conclusion and Future Woks • Forwarding using priori label or social structure inferred through observation • Distributed k-clique building through gossiping • Why per-6-hour? • Weighted version of k-clique detection • Third generation modeling Imperial College London

  35. Jon.Crowcroft@cl.cam.ac.uk Imperial College London

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