Hi,

I have a question about the meta-path function for heterogeneous graphs. Does that meta-path function support the weighted graph? or Do you have the plan to develop a weighted meta-path function?

Hi,

I have a question about the meta-path function for heterogeneous graphs. Does that meta-path function support the weighted graph? or Do you have the plan to develop a weighted meta-path function?

What kind of weighted meta-path functions do you want?

Hi, the method of this paper ’ Weighted Random Walks for Meta-Path Expansion in Heterogeneous Networks’.

Basically you want to sample edges based on their weights for some edge types in random-walk meta-path generation. I believe DGL has not supported something like this.

OK. Thank you. BTW, Do you have the plan to develop such kinds of APIs to support the weighted graph better?

I checked the paper you mentioned. Based on my understanding, the random walk is not about a “weighted metapath”. Rather, it is just a normal random walk based on a *given* metapath, with uniform transition probability on all other edge types except the “user-item” edge type.

In this case, `dgl.random_walk`

already supports metapath-based random walk with non-uniform transition probabilities.

For weighted random walk that generates meta path, you will need to assign transition probabilities to the metagraph itself and use `dgl.random_walk`

to generate the said metapath.

Thank you. Yes. That’s what I need. I did not know dgl has embedded the API of random_walk.