Difference between revisions of "Seshadri et at KDD'08"
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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. | 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. | ||
| − | The DPLN is based on Geometric Brownian Motion, which is widely used in financial field in modeling the stock movement. It is based on a wiener process, and the exponential term guarantees that the entries will never be less than zero -- the name log normal comes from both wiener process (we can view it as a normal distribution) and the exponential term. | + | The DPLN is based on Geometric Brownian Motion, which is widely used in financial field in modeling the stock movement. The formula for a Geometric Brownian Motion is: |
| + | |||
| + | <math> | ||
| + | dS_t=\muS_tdt+\sigmaS_tdw_t | ||
| + | <math> | ||
| + | |||
| + | It is based on a wiener process, and the exponential term guarantees that the entries will never be less than zero -- the name log normal comes from both wiener process (we can view it as a normal distribution) and the exponential term. | ||
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. | 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. | ||
Revision as of 19:09, 4 February 2011
Citation
Seshadri, Mukund and Machiraju, Sridhar and Sridharan, Ashwin and Bolot, Jean and Faloutsos, Christos and Leskove, Jure. Mobile call graphs: beyond power-law and lognormal distributions. In KDD'08
Summary
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.
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.
The DPLN is based on Geometric Brownian Motion, which is widely used in financial field in modeling the stock movement. The formula for a Geometric Brownian Motion is:
<math> dS_t=\muS_tdt+\sigmaS_tdw_t <math>
It is based on a wiener process, and the exponential term guarantees that the entries will never be less than zero -- the name log normal comes from both wiener process (we can view it as a normal distribution) and the exponential term.
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.
