t ime series modeling of temporal network sandipan sikdar cnerg retreat 14 1
TRANSCRIPT
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TIME SERIES MODELING OF TEMPORAL NETWORK
Sandipan Sikdar
CNeRG Retreat ‘14
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TEMPORAL NETWORK
Network that changes with time
Nodes and edges entering or leaving the system dynamically
Example: Human communication network, mobile call network
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TEMPORAL NETWORK AS TIME SERIES
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TEMPORAL NETWORK AS TIME SERIES
Time series of some of the properties
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PROPERTIES OF TIME SERIES
Stationarity ADF test KPSS test
Trend
Periodicity
Seasonality
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FORECASTING USING TIME SERIES
Selecting a window
Auto-correlation ARIMA (auto-regressive-integrated-moving-
average)
Auto-correlation function plots
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RESULTS AND DISCUSSIONS
Datasets:
INFOCOM ’06Human communication network collected at IEEE INFOCOM 2006
SIGCOMM ’09Human communication network collected at SIGCOMM 2009
Resolution – 5 minutes.
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ACCURACY OF PREDICTION
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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.
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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.
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SPECTROGRAM ANALYSIS
Spectrograms for (a)no of nodes and (b)betweenness centrality
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SPREADING IN TEMPORAL NETWORKS
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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
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BROADCAST/SPREADING IN DTN
Challenges:o Distributed Systemo No global informationo Unstable links
Routing mechanisms:o Spray and waito Two-hop spreading
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THE OVERALL SETUP
Agent configuration and network
Message configuration
Transfer protocol Push Pull (restricted)
Metrics of interest Delay Wastage
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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?
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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
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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
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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?
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THANK YOU……