CoDy: Counterfactual Explainers for Dynamic Graphs
Authors: Zhan Qu, Daniel Gomm, Michael Färber
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments against state-of-the-art factual and counterfactual baselines demonstrate Co Dy s effectiveness, with improvements of 16% in AUFSC+ over the strongest baseline. Our code is available at: https://github.com/daniel-gomm/Co Dy |
| Researcher Affiliation | Collaboration | *Equal contribution 1TU Dresden, Dresden, Germany 2Sca DS.AI, Dresden, Germany 3Karlsruhe Institute of Technology, Karlsruhe, Germany 4University of Amsterdam, Amsterdam, Netherlands 5Centrum Wiskunde en Informatica, Amsterdam, Netherlands. Correspondence to: Daniel Gomm <EMAIL>, Zhan Qu <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Search algorithm of Co Dy. Input: TGNN model f, input graph G, explained event εi, selection policy δ, max iterations itmax Output: best explanation found porig f(G(ti), εi) nroot ( , null, null, , 0, null, 1) it 0 while it < itmax and nroot is selectable do nselected select(nroot, δ) simulate(nselected, f, G, εi) expand(nselected, porig) backpropagate(parentselected) it it + 1 end nbest select best(nroot) return sbest |
| Open Source Code | Yes | Our code is available at: https://github.com/daniel-gomm/Co Dy |
| Open Datasets | Yes | We evaluate on three datasets: Wikipedia (Kumar et al., 2019), UCI-Messages (Kunegis, 2013), and UCIForums (Kunegis, 2013). |
| Dataset Splits | No | The paper mentions performance for Transductive/Inductive settings and evaluates instances where the TGNN makes correct versus incorrect predictions, but it does not specify explicit training/validation/test split percentages, sample counts, or refer to standard predefined splits with specific details (e.g., "80/10/10 split" or "standard splits from X benchmark"). |
| Hardware Specification | Yes | For replicability, we run the experiments on a high-performance computing cluster with an Intel Xeon Gold 6230 CPU, 16GB of RAM, and an NVIDIA Tesla V100 SXM2 GPU with 32GB of VRAM. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We train the TGN model using the TGNattn configuration from the original paper (Rossi et al., 2020). [...] We configure Gree Dy with a candidate event limit of 64, sampling up to 10 events per iteration. For Co Dy, we also limit the search space to 64 events, with a maximum of 300 iterations and α = 2/3 to emphasize exploration over exploitation. Appendix G shows a sensitivity analysis on these parameters. |