We lifted 5 functions out of this paper's own repositories and ran 5 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 |
|---|---|---|
| jaygagaga/pronog | canonical | 4 of 4 |
| copy not recorded | — | 1 of 1 |
| Function | Status | Where it lives |
|---|---|---|
| averageemb | Ran | jaygagaga/pronog/downprompt_metanet3.py pointer only (licence: NONE) · get_code("5b1e08138a13d5ec") |
| extract_args_from_json | Ran | jaygagaga/pronog/DSSL/ENZYMES_pretrain.py pointer only (licence: NONE) · get_code("fd09b516ae8e2395") |
| load_data | Ran | jaygagaga/pronog/DSSL/ENZYMES_pretrain.py pointer only (licence: NONE) · get_code("3904db7d99ca426e") |
| parse_index_file | Ran | this paper's copy was not recorded; identical code first harvested from DSL-Lab/Specformer pointer only · get_code("c1d6392f89c5e495") |
| split_and_batchify_graph_feats | Ran | jaygagaga/pronog/downprompt_metanet3.py pointer only (licence: NONE) · get_code("7c37491a7f04588e") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graphs are ubiquitous for modeling complex relationships between objects across various fields. Graph neural networks (GNNs) have become a mainstream technique for graph-based applications, but their performance heavily relies on abundant labeled data. To reduce labeling requirement, pre-training and prompt learning has become a popular alternative. However, most existing prompt methods do not differentiate homophilic and heterophilic characteristics of real-world graphs. In particular, many real-world graphs are non-homophilic, not strictly or uniformly homophilic with mixing homophilic and heterophilic patterns, exhibiting varying non-homophilic characteristics across graphs and nodes. In this paper, we propose ProNoG, a novel pre-training and prompt learning framework for such non-homophilic graphs. First, we analyze existing graph pre-training methods, providing theoretical insights into the choice of pre-training tasks. Second, recognizing that each node exhibits unique non-homophilic characteristics, we propose a conditional network to characterize the node-specific patterns in downstream tasks. Finally, we thoroughly evaluate and analyze ProNoG through extensive experiments on ten public datasets.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2408.12594")
get_code_for_paper("2408.12594")
have("2408.12594")
Connect an agent — have() is free.