Learning generative models of graphs

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I am trying to understand a research paper bit by bit and I am stuck trying to understand this paragraph.
What I have understood from the para-
We have a set of graphs G=G1,G2,.. which have been created by different distributions such as pdf cdf etc based on sample data.The graphs can have any node ordering function pie as adjacency matrix can be created in any order.Now our generative model wants to learn this distribution therefore takes the sample data from which graph was generated and the distribution such as cdf and then learns fr0m it using in this case generative models.
Any help in this regard is appreciated.Thanks!
statistics graph-theory random-graphs
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up vote
0
down vote
favorite
I am trying to understand a research paper bit by bit and I am stuck trying to understand this paragraph.
What I have understood from the para-
We have a set of graphs G=G1,G2,.. which have been created by different distributions such as pdf cdf etc based on sample data.The graphs can have any node ordering function pie as adjacency matrix can be created in any order.Now our generative model wants to learn this distribution therefore takes the sample data from which graph was generated and the distribution such as cdf and then learns fr0m it using in this case generative models.
Any help in this regard is appreciated.Thanks!
statistics graph-theory random-graphs
Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44
add a comment |Â
up vote
0
down vote
favorite
up vote
0
down vote
favorite
I am trying to understand a research paper bit by bit and I am stuck trying to understand this paragraph.
What I have understood from the para-
We have a set of graphs G=G1,G2,.. which have been created by different distributions such as pdf cdf etc based on sample data.The graphs can have any node ordering function pie as adjacency matrix can be created in any order.Now our generative model wants to learn this distribution therefore takes the sample data from which graph was generated and the distribution such as cdf and then learns fr0m it using in this case generative models.
Any help in this regard is appreciated.Thanks!
statistics graph-theory random-graphs
I am trying to understand a research paper bit by bit and I am stuck trying to understand this paragraph.
What I have understood from the para-
We have a set of graphs G=G1,G2,.. which have been created by different distributions such as pdf cdf etc based on sample data.The graphs can have any node ordering function pie as adjacency matrix can be created in any order.Now our generative model wants to learn this distribution therefore takes the sample data from which graph was generated and the distribution such as cdf and then learns fr0m it using in this case generative models.
Any help in this regard is appreciated.Thanks!
statistics graph-theory random-graphs
statistics graph-theory random-graphs
asked Sep 10 at 10:37
ubuntu_noob
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Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44
add a comment |Â
Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44
Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44
Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44
add a comment |Â
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Almost understood. There is a distribution from grahs p(G), which is unknown to you, but you observe a number samples from it, G1, G2, ..., Gs. The generative model will use those samples to learn p_model(G), an approximation of p(G). If you are able to learn correctly, you can sample from p_model(G) being confident that the graphs you sample are very similar to graphs you would have sampled if you knew p(G) from the get-go.
– mp85
Sep 11 at 21:44