Open sadrahkm opened 5 months ago
For heterogeneous graphs, data leakage is prevent via specifying "reverse" edge types:
edge_types=("user", "rates", "movie"),
rev_edge_types=("movie", "rev_rates", "user")
This makes sure that links are eliminated in the reverse edge type as well.
🐛 Describe the bug
I am working on a task in which I have two types of nodes and the edges are only
association
, so it is considered a bipartite graph. I want this graph to be undirected so the message passing can be done in both directions. But I recently noticed that the documentation has mentioned thatis_undirected
option doesn't work when we have a bipartite graph, Did I understand this right?If I am correct, so the example written in this blog post would be wrong. Because in that example, there is exactly a similar situation as mine (undirected bipartite graph), and the
is_undirected=True
cannot be used to avoid data leakage. If so, is there any way to fix this issue?I would appreciate if you clarify since this I believe this is an important problem.
Versions
PyTorch version: 2.2.0+cu121 Is debug build: False CUDA used to build PyTorch: 12.1 ROCM used to build PyTorch: N/A
OS: Debian GNU/Linux 12 (bookworm) (x86_64) GCC version: (Debian 12.2.0-14) 12.2.0 Clang version: Could not collect CMake version: version 3.25.1 Libc version: glibc-2.36
Python version: 3.11.2 (main, Mar 13 2023, 12:18:29) [GCC 12.2.0] (64-bit runtime) Python platform: Linux-6.1.0-21-amd64-x86_64-with-glibc2.36 Is CUDA available: True CUDA runtime version: Could not collect CUDA_MODULE_LOADING set to: LAZY GPU models and configuration: GPU 0: NVIDIA A16 GPU 1: NVIDIA A16 GPU 2: NVIDIA A16 GPU 3: NVIDIA A16
Nvidia driver version: 525.147.05 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Is XNNPACK available: True ...