I’m trying to reproduce the model in a paper Pre-training on Large-Scale Heterogeneous Graph. Basically, it’s a heterogeneous graph contrastive learning method based on Personalized PageRank. To perform PPNP on heterogeneous, they `obtain personalized PageRank Π^𝑅 for a relation 𝑅 by iteratively updating the following:`

I don’t understand the following points：

- What’t the initial state of the
`iteration`

before`updating`

- what’s the dimension of Π^𝑅 and Π^𝑅(-1), in my opinion, it should match the number of nodes of the corresponding A_1 & A_2 type.
- Correspondingly, how can the symmetric (by definition) unix matrix
**I**be added to the asymmetric Π^𝑅 (whose dimension should match the number of nodes of the corresponding A_1 & A_2 type)

I email to the authors but haven’t been replied, so I have to seek help here. It would be great if someone familiar with this model could reply to me. Thanks in advance!