Xikun Zhang, Jingchao Ni, Dongjin Song, Guiquan Sun
We lifted 9 functions out of this paper's own repositories and ran 7 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 |
|---|---|---|
| UConn-DSIS/DRIFT | canonical | 7 of 9 |
| Function | Status | Where it lives |
|---|---|---|
| add_memory_grad | Ran | UConn-DSIS/DRIFT/Baselines/gss_model.py pointer only (licence: NONE) · get_code("3980e3db08651b42") |
| compute_centers | Ran | UConn-DSIS/DRIFT/gaussian_utils.py pointer only (licence: NONE) · get_code("0e4a15547d1dbd7b") |
| gaussian_task_weights | Ran | UConn-DSIS/DRIFT/gaussian_utils.py pointer only (licence: NONE) · get_code("ea19b36bec167806") |
| get_grad_vector | Ran | UConn-DSIS/DRIFT/Baselines/gss_model.py pointer only (licence: NONE) · get_code("db86aa783c4103d0") |
| normalized_mixing_entropy | Ran | UConn-DSIS/DRIFT/gaussian_utils.py pointer only (licence: NONE) · get_code("9646c835abc89492") |
| task_changes | Ran | UConn-DSIS/DRIFT/metrics.py pointer only (licence: NONE) · get_code("05137c22e8be0efc") |
| tf_metrics | Ran | UConn-DSIS/DRIFT/metrics.py pointer only (licence: NONE) · get_code("5e131821209f5df2") |
| confusion_matrix | Not yet run | UConn-DSIS/DRIFT/metrics.py pointer only (licence: NONE) · get_code("a115379e55454091") |
| get_model | Not yet run | UConn-DSIS/DRIFT/Backbones/model_factory.py pointer only (licence: NONE) · get_code("a5ea5eeb095d63b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Continual graph learning (CGL) aims to learn from dynamically evolving graphs while mitigating catastrophic forgetting. Existing CGL approaches typically adopt a task-based formulation, where the data stream is partitioned into a sequence of discrete tasks with pre-defined boundaries. However, such assumptions rarely hold in real-world environments, where data distributions evolve continuously and task identity is often unavailable. To better reflect realistic non-stationary environments, we revisit continual graph learning from a task-free perspective. We propose a unified formulation that models the data stream as a time-varying mixture of latent task distributions, enabling continuous modeling of distribution drift. Based on this formulation, we construct DRIFT, a benchmark that spans a spectrum of transition dynamics ranging from hard task switches to smooth distributional drift through a Gaussian parameterization. We evaluate representative continual learning methods under this task-free setting and observe substantial performance degradation compared to traditional task-based protocols. Our findings indicate that many existing approaches implicitly rely on task boundary information and struggle under realistic task-free graph streams. This work highlights the importance of studying continual graph learning under realistic non-stationary conditions and provides a benchmark for future research in this direction. Our code is available at https://github.com/UConn-DSIS/DRIFT.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2605.12998")
get_code_for_paper("2605.12998")
have("2605.12998")
Connect an agent — have() is free.