We lifted 6 functions out of this paper's own repositories and ran 3 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| lenscloth/grpe | canonical | 3 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| evaluate | Ran | lenscloth/grpe/zinc.py code served (permissive licence) · get_code("9266294d3f2bade7") |
| is_left_better | Ran | lenscloth/grpe/gpp.py code served (permissive licence) · get_code("13f0b879ee9b94c4") |
| train | Ran | lenscloth/grpe/zinc.py code served (permissive licence) · get_code("9ec5aa5e7b77d9b7") |
| gather_uneven_tensors | Not yet run | lenscloth/grpe/grpe/distributed.py code served (permissive licence) · get_code("9ff14674677c2429") |
| load_pretrained_fingerprint | Not yet run | lenscloth/grpe/grpe/pretrained.py code served (permissive licence) · get_code("4b3aa1f322c9bfcb") |
| train | Not yet run | lenscloth/grpe/gpp.py code served (permissive licence) · get_code("79c9fa759ce09d31") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We propose a novel positional encoding for learning graph on Transformer architecture. Existing approaches either linearize a graph to encode absolute position in the sequence of nodes, or encode relative position with another node using bias terms. The former loses preciseness of relative position from linearization, while the latter loses a tight integration of node-edge and node-topology interaction. To overcome the weakness of the previous approaches, our method encodes a graph without linearization and considers both node-topology and node-edge interaction. We name our method Graph Relative Positional Encoding dedicated to graph representation learning. Experiments conducted on various graph datasets show that the proposed method outperforms previous approaches significantly. Our code is publicly available at https://github.com/lenscloth/GRPE.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2201.12787")
get_code_for_paper("2201.12787")
have("2201.12787")
Connect an agent — have() is free.