Comments (2)
marcopodda
commented on September 12, 2024
Hi,
edge[0] contains a node_id. indicator[edge[0]] maps the node_id to the associated graph_id using the graph indicator file. As per the readme:
(2) DS_graph_indicator.txt (n lines)
column vector of graph identifiers for all nodes of all graphs, the value in the i-th line is the graph_id of the node with node_id i.
Therefore, the preprocessing is correct. Also, note that edge[0] does not necessarily reflect the source node of the edge; it depends on the assumption made in the adjacency list file.
Thanks for pointing out this ambiguity, we have added appropriate comments to clarify.
from gnn-comparison.
JindongGu
commented on September 12, 2024
Therefore
Thank you for your explanations. I misunderstood the dataset.
Also thank you for your nice contributions, these fair comparisons of GNNs is of significance to our community.
from gnn-comparison.
Related Issues (20)
- Best configs / splits HOT 3
- It takes long time to train DiffPool HOT 1
- The program crashes without "--debug" flag being set when using Cuda. HOT 1
- Using DGCNN with new data HOT 1
- Using dropout in last layer HOT 1
- Call each other HOT 2
- additional features HOT 1
- PyTorch -- multiple threads for each process HOT 2
- error message HOT 1
- OUTER_TS is 20.0 FOR M5k(GraphSAGE) HOT 2
- Different best config in each k_fold HOT 6
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- DiffPool breaks on ENZYMES? HOT 5
- GraphSage doesn't learn. HOT 12
- Results don't match - am I running something in the wrong way? HOT 2
- Not a question, the code is running well. Thanks.
- the dataset for the experiments HOT 1
- Multi-process running errors HOT 4
- The position of "config_id += 1" in the 84 of evaluation/model_selection/HoldOutSelector.py HOT 1
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