Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
State Space Models on Temporal Graphs: A First-Principles Study
Authors: Jintang Li, Ruofan Wu, Xinzhou Jin, Boqun Ma, Liang Chen, Zibin Zheng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this work, we undertake a principled investigation that extends SSM theory to temporal graphs by integrating structural information into the online approximation objective via the adoption of a Laplacian regularization term. The emergent continuous-time system introduces novel algorithmic challenges, thereby necessitating our development of GRAPHSSM, a graph state space model for modeling the dynamics of temporal graphs. Extensive experimental results demonstrate the effectiveness of our GRAPHSSM framework across various temporal graph benchmarks. |
| Researcher Affiliation | Collaboration | Jintang Li1 , Ruofan Wu2 , Xinzhou Jin1, Boqun Ma3, Liang Chen1 , Zibin Zheng1 1Sun Yat-sen University, 2Coupang, 3Shanghai Jiao Tong University |
| Pseudocode | Yes | A detailed exposition of the GRAPHSSM-S4 (resp. GRAPHSSM-S5, GRAPHSSM-S6) layer is provided in algorithm 1 (resp. algorithm 2, algorithm 3) in appendix D.2. |
| Open Source Code | Yes | Code will be made available at https: //github.com/Edison Leeeee/Graph SSM. |
| Open Datasets | Yes | The experiments are conducted on six temporal graph benchmarks with different scales and time snapshots, including DBLP-3 [47], Brain [47], Reddit [47], DBLP-10 [25], ar Xiv [19], and Tmall [25]. |
| Dataset Splits | Yes | For the DBLP-3, Brain, and Reddit datasets, we adopt the 81%/9%/10% train/validation/test splits as suggested in [47]. |
| Hardware Specification | Yes | All experiments are conducted on an NVIDIA RTX 3090 Ti GPU with 24 GB memory. |
| Software Dependencies | No | The paper states: 'We implement our models as well as baselines with Py Torch [35] and Py Torch Geometric [5]'. However, it does not specify version numbers for these software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | For the DBLP-3, Brain, and Reddit datasets, we adopt the 81%/9%/10% train/validation/test splits as suggested in [47]. For the DBLP-10 and Tmall datasets, we follow the experimental settings of previous works [25], where 80% of the nodes are randomly selected as the training set, and the remaining nodes are used as the test set. We employ feature mixing for DBLP-10 and representation mixing for other datasets. The graph convolution networks used to learn the graph structure are SAGE [16] for all datasets, except for ar Xiv, where GCN [21] is used. |