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 | Conference PDF | Archive PDF | Plain Text | 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. |