Difference between revisions of "Steve Hanneke and Eric Xing, Discrete Temporal Models of Social Networks"
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== '''Datasets''' == | == '''Datasets''' == | ||
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| + | The data involved in this example come from the [[Dateset::United States 108th Senate, having n = 100 actors.]] Every time a proposal is made in the Senate, be it | ||
| + | a bill, amendment, resolution, etc., a single Senator serves as the proposal’s sponsor and there may possibly be several cosponsors. | ||
== '''Summary''' == | == '''Summary''' == | ||
| − | This paper proposed a discrete temporal model family that is capable of modeling network evolution, while maintaining the flexibility of ERGMs. It also proposed such models to build upon ERGMs as much as possible. | + | This [[Category::paper]] proposed a discrete temporal model family that is capable of modeling network evolution, while maintaining the flexibility of ERGMs. It also proposed such models to build upon ERGMs as much as possible. |
At first, this paper show us the simplest case of the proposed models before turning to the fully general models. Then this paper gives a general model and use MLE to estimate the parameter θ，which is the only unknown value in this model. The proposed model is also tested in order to prove its generality in practical work | At first, this paper show us the simplest case of the proposed models before turning to the fully general models. Then this paper gives a general model and use MLE to estimate the parameter θ，which is the only unknown value in this model. The proposed model is also tested in order to prove its generality in practical work | ||
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== ''' Model Desprition ''' == | == ''' Model Desprition ''' == | ||
Revision as of 19:55, 27 March 2011
Citation
Steve Hanneke and Eric Xing, Discrete Temporal Models of Social Networks. Workshop on Statistical Network Analysis, held at the 23 rd International Conference on Machine Learning, 2006.
Online Version
Discrete Temporal Models of Social Networks
Datasets
The data involved in this example come from the United States 108th Senate, having n = 100 actors. Every time a proposal is made in the Senate, be it a bill, amendment, resolution, etc., a single Senator serves as the proposal’s sponsor and there may possibly be several cosponsors.
Summary
This paper proposed a discrete temporal model family that is capable of modeling network evolution, while maintaining the flexibility of ERGMs. It also proposed such models to build upon ERGMs as much as possible. At first, this paper show us the simplest case of the proposed models before turning to the fully general models. Then this paper gives a general model and use MLE to estimate the parameter θ，which is the only unknown value in this model. The proposed model is also tested in order to prove its generality in practical work
