We lifted 8 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 |
|---|---|---|
| Microsoft/graph-partition-neural-network-samples | canonical | 5 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| check_symmetric | Ran | Microsoft/graph-partition-neural-network-samples/gpnn/utils/spectral_graph_partition.py code served (permissive licence) · get_code("13b061acfb93da40") |
| compute_laplacian | Ran | Microsoft/graph-partition-neural-network-samples/gpnn/utils/spectral_graph_partition.py code served (permissive licence) · get_code("bc064b53266fde74") |
| compute_outgoing_degree | Ran | Microsoft/graph-partition-neural-network-samples/gpnn/utils/flood_fill_partition.py code served (permissive licence) · get_code("4d2562e39ad678d1") |
| construct_adj_mat | Ran | Microsoft/graph-partition-neural-network-samples/gpnn/utils/spectral_graph_partition.py code served (permissive licence) · get_code("e00c4c0c5ab57eeb") |
| get_seeds_semi_supervised_rand | Ran | Microsoft/graph-partition-neural-network-samples/gpnn/utils/flood_fill_partition.py code served (permissive licence) · get_code("09b1fdade5271399") |
| aggregate | Not yet run | Microsoft/graph-partition-neural-network-samples/gpnn/model/model_helper.py code served (permissive licence) · get_code("b4d3f127a57bf846") |
| multi_seed_flood_fill | Not yet run | Microsoft/graph-partition-neural-network-samples/gpnn/utils/flood_fill_partition.py code served (permissive licence) · get_code("1809f4d316300354") |
| weight_variable | Not yet run | Microsoft/graph-partition-neural-network-samples/gpnn/model/nn_cells.py code served (permissive licence) · get_code("3fe890ee11cb4fad") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present graph partition neural networks (GPNN), an extension of graph neural networks (GNNs) able to handle extremely large graphs. GPNNs alternate between locally propagating information between nodes in small subgraphs and globally propagating information between the subgraphs. To efficiently partition graphs, we experiment with several partitioning algorithms and also propose a novel variant for fast processing of large scale graphs. We extensively test our model on a variety of semi-supervised node classification tasks. Experimental results indicate that GPNNs are either superior or comparable to state-of-the-art methods on a wide variety of datasets for graph-based semi-supervised classification. We also show that GPNNs can achieve similar performance as standard GNNs with fewer propagation steps.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1803.06272")
get_code_for_paper("1803.06272")
have("1803.06272")
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