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| | ==Citation == | | ==Citation == |
| − | Seshadri, Mukund and Machiraju, Sridhar and Sridharan, Ashwin and Bolot, Jean and Faloutsos, Christos and Leskove, Jure.
| + | Leman Akoglu, Bhavana Dalvi Structure, Tie Persistence and Event Detection in Large Phone and SMS Networks |
| − | Mobile call graphs: beyond power-law and lognormal distributions.
| + | In KDD'10 |
| − | In KDD'08 | |
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| | ==Summary== | | ==Summary== |
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| − | This is a [[paper]] about model fitting. The authors found that [[power law]], which is widely used in the society of social media analysis, sometimes might not fit the data well. The authors developed a method to fit the data to a Double Pareto LogNormal (DPLN) distribution instead. They demonstrated their method through a massive phone call dataset which has more than one million users. | + | This [[paper]] focuses on the same phone call data that we are going to analyze. The paper tries to answer three questions: First, what is the structural property of the dataset? What are the relationships between different quantities taken from the egonet? Second, if a link exists between two people, will the link still exist in the future? Third, how to detect change-point anomalies? |
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| | ==Brief Description== | | ==Brief Description== |
| − | The authors are interested in three metrics: the number of phone calls per customer, the total talk minutes per customer and the number of callers per customer. The first and last metrics are related to out-degrees, and the second metric is unique in a phone call dataset.
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| − | The DPLN is based on Geometric Brownian Motion, which is widely used in financial field in modeling the stock movement as time passes by. The formula for a Geometric Brownian Motion is:
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| − | <math>
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| − | dS_t=\mu S_t dt+\sigma S_t dw_t
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| − | </math>
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| − | Here <math>w_t</math> represents a wiener process, which is a continuous markov process with independent increment. The randomness comes from this term. we can understand <math>\mu</math> as the drift term and <math>\sigma</math> as the variance term. One important result for Geometric Brownian Motion is that <math> \frac{S_t}{S_0}</math> follows a log-normal distribution.
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| − | By introducing the randomness the authors obtain a very good fit for the data.
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| − | The fit can be used for anomaly detection and pricing structural design. The authors are also interested in the evolution of data over time (They refer it by a generative process). They sliced the time into discrete pieces, and looked at the ratio of each metrics over time. Based on the result of the fit, a lognormal multiplicative process fits the data very well.
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Revision as of 21:47, 15 February 2011
Citation
Leman Akoglu, Bhavana Dalvi Structure, Tie Persistence and Event Detection in Large Phone and SMS Networks
In KDD'10
Summary
This paper focuses on the same phone call data that we are going to analyze. The paper tries to answer three questions: First, what is the structural property of the dataset? What are the relationships between different quantities taken from the egonet? Second, if a link exists between two people, will the link still exist in the future? Third, how to detect change-point anomalies?
Brief Description
