We lifted 3 functions out of this paper's own repositories and ran 0 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 |
|---|---|---|
| CongWeilin/SGCN | canonical | 0 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| fastgcn_sampler | Not yet run | CongWeilin/SGCN/samplers.py pointer only (licence: NONE) · get_code("a0bf27d81426f7c8") |
| graphsage_sampler | Not yet run | CongWeilin/SGCN/samplers.py pointer only (licence: NONE) · get_code("c798cdb76f268f5b") |
| ladies_sampler | Not yet run | CongWeilin/SGCN/samplers.py pointer only (licence: NONE) · get_code("ed50c26965006813") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite their great success, training GCNs on large graphs suffers from computational and memory issues. A potential path to circumvent these obstacles is sampling-based methods, where at each layer a subset of nodes is sampled. Although recent studies have empirically demonstrated the effectiveness of sampling-based methods, these works lack theoretical convergence guarantees under realistic settings and cannot fully leverage the information of evolving parameters during optimization. In this paper, we describe and analyze a general doubly variance reduction schema that can accelerate any sampling method under the memory budget. The motivating impetus for the proposed schema is a careful analysis of the variance of sampling methods where it is shown that the induced variance can be decomposed into node embedding approximation variance (zeroth-order variance) during forward propagation and layerwise-gradient variance (first-order variance) during backward propagation. We theoretically analyze the convergence of the proposed schema and show that it enjoys an $\mathcal{O}(1/T)$ convergence rate. We complement our theoretical results by integrating the proposed schema in different sampling methods and applying them to different large real-world graphs.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2103.02696")
get_code_for_paper("2103.02696")
have("2103.02696")
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