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Time series modeling of temporal network

Time series modeling of temporal network. Sandipan Sikdar CNeRG Retreat ‘14. Temporal network. Network that changes with time Nodes and edges entering or leaving the system dynamically Example: Human communication network, mobile call network. Temporal network as time series.

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Time series modeling of temporal network

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  1. Time series modeling of temporal network SandipanSikdar CNeRG Retreat ‘14

  2. Temporal network • Network that changes with time • Nodes and edges entering or leaving the system dynamically • Example: Human communication network, mobile call network

  3. Temporal network as time series

  4. Temporal network as time series Time series of some of the properties

  5. Properties of time series • Stationarity • ADF test • KPSS test • Trend • Periodicity • Seasonality

  6. Forecasting using Time series • Selecting a window • Auto-correlation • ARIMA (auto-regressive-integrated-moving-average) Auto-correlation function plots

  7. Results and discussions • Datasets: • INFOCOM ’06 Human communication network collected at IEEE INFOCOM 2006 • SIGCOMM ’09 Human communication network collected at SIGCOMM 2009 Resolution – 5 minutes.

  8. Accuracy of prediction

  9. Spectrogram analysis • Short term Fourier transform • The whole series is divided into equal-sized windows and discrete Fourier transform is applied on this windowed data. • We are able to get a view of the local frequency spectrum.

  10. Spectrogram analysis • We use spectrograms for two purposes • Determining predictability of a property • We look into the spectrogram of the whole series. • Determining the goodness of prediction at any time point • We look into the spectrogram of the series formed by the previous few points.

  11. Spectrogram analysis Spectrograms for (a)no of nodes and (b)betweenness centrality

  12. Spreading in temporal networks

  13. Delay-tolerant networks (DTN) • Very sparse node population • Unequal delay associated between the occurrence and reoccurrence of a link • Lack of full network connectivity at virtually all points in time • Eventual packet delivery achieved through node mobility

  14. Broadcast/Spreading in DTN Challenges: • Distributed System • No global information • Unstable links Routing mechanisms: • Spray and wait • Two-hop spreading

  15. The overall setup • Agent configuration and network • Message configuration • Transfer protocol • Push • Pull (restricted) • Metrics of interest • Delay • Wastage

  16. a combined strategy • Push strategy works best at the start • Pull strategy works best towards the end • Can we combine the two strategies to improve broadcast time? • Can we modify the strategies to reduce wastage?

  17. The x% strategy • Switch from push to pull when x% of the nodes have been covered • Significant improvement in broadcast delay and wastage Need for a global information and also spread the information to all the nodes

  18. Another strategy • Interleave between push and pull • A node starts the broadcast • Capable nodes push for a preset number of time-steps • Number of steps changes dynamically • Nodes with partial segment tries to pull in the next few steps

  19. Reduce wastage • Keeping some history information at each node • An array which keeps track of the last k contact opportunities • If last k transactions were unsuccessful, we turn off the node with some probability • Reduces wastage • But convergence?

  20. Thank you……

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