Hao Peng, Philip Yu, Sheng Zhou, Beining Yang, Qingyun Sun, Xingcheng Fu, Jianxin Li, Ziying Chen, Cheng Ji, Benchmark Datasets
We lifted 5 functions out of this paper's own repositories and ran 4 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 |
|---|---|---|
| RingBDStack/GC-Bench | canonical | 3 of 3 |
| ringbdstack/gc-bench | canonical | 1 of 2 |
| Function | Status | Where it lives |
|---|---|---|
| generate_labels_syn | Ran | ringbdstack/gc-bench/evaluator/test_other_tasks.py code served (permissive licence) · get_code("7c6ae6b3318675a1") |
| get_akx | Ran | RingBDStack/GC-Bench/networks_gc/dense_sgc.py code served (permissive licence) · get_code("5b8e30db4dbc3780") |
| get_mgrid | Ran | RingBDStack/GC-Bench/networks_nc/IGNR.py code served (permissive licence) · get_code("d0e91a77fdc73c4f") |
| load_config | Ran | RingBDStack/GC-Bench/coreset/configs.py code served (permissive licence) · get_code("a2d97b7ae1e59b60") |
| normalize | Not yet run | ringbdstack/gc-bench/evaluator/test_other_tasks.py code served (permissive licence) · get_code("5e1542d99d3cc83d") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Graph condensation (GC) has recently garnered considerable attention due to its ability to reduce large-scale graph datasets while preserving their essential properties. The core concept of GC is to create a smaller, more manageable graph that retains the characteristics of the original graph. Despite the proliferation of graph condensation methods developed in recent years, there is no comprehensive evaluation and in-depth analysis, which creates a great obstacle to understanding the progress in this field. To fill this gap, we develop a comprehensive Graph Condensation Benchmark (GC-Bench) to analyze the performance of graph condensation in different scenarios systematically. Specifically, GC-Bench systematically investigates the characteristics of graph condensation in terms of the following dimensions: effectiveness, transferability, and complexity. We comprehensively evaluate 12 state-of-the-art graph condensation algorithms in node-level and graphlevel tasks and analyze their performance in 12 diverse graph datasets. Further, we have developed an easy-to-use library for training and evaluating different GC methods to facilitate reproducible research. The GC-Bench library is available at https://github.com/RingBDStack/GC-Bench.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2407.00615")
get_code_for_paper("2407.00615")
have("2407.00615")
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