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..
Neural Spacetimes for DAG Representation Learning
Authors: Haitz Sáez de Ocáriz Borde, Anastasis Kratsios, Marc T Law, Xiaowen Dong, Michael Bronstein
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our framework computationally with synthetic weighted DAGs and real-world network embeddings; in both cases, the NSTs achieve lower embedding distortions than their counterparts using fixed spacetime geometries. ... We experimentally validate our model on synthetic metric DAG datasets, as well as real-world directed graphs that involve web hyperlink connections and gene expression networks, respectively. ... Section 4 EXPERIMENTAL RESULTS |
| Researcher Affiliation | Collaboration | Haitz S aez de Oc ariz Borde University of Oxford Anastasis Kratsios Mc Master University & Vector Institute Marc T. Law NVIDIA Xiaowen Dong University of Oxford Michael Bronstein University of Oxford & AITHYRA |
| Pseudocode | Yes | Algorithm 1: Neural (quasi-)metric, D Algorithm 2: Neural Partial Order, T Algorithm 3: Neural Spacetime, S = (E, D, T ) (Forward Pass) |
| Open Source Code | No | The paper does not explicitly provide an open-source code link or state that code is available in supplementary materials. It mentions using 'Network X library' but not its own code. |
| Open Datasets | Yes | We test our approach on real-world networks. In Table 2, we present results for the Cornell, Texas, and Wisconsin (Web KB) datasets (Rozemberczki et al., 2021)... We also work with real-world gene regulatory network datasets (Marbach et al., 2012)... We conduct an additional experiment on the ogbn-arxiv dataset (Hu et al., 2021) |
| Dataset Splits | Yes | We reimplement all baselines and test them on the Cornell, Texas, and Wisconsin datasets using 10-fold splits, based on the masks provided in Py Torch Geometric. |
| Hardware Specification | No | The paper mentions general computing environments for experiments (e.g., 'on a GPU', 'on a server') but does not specify any particular hardware details such as exact GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions 'Network X library', 'Adam W optimizer', and 'Py Torch Geometric' but does not specify any version numbers for these software components. |
| Experiment Setup | Yes | We employ a batch size of 10,000 to learn the distances, train for 10 epochs with a learning rate of 3x10^-3 and Adam W optimizer, and apply a max gradient norm of 1. All encoders have a total of 10 hidden layers with 100 neurons and a final projection layer to the embedding dimension. The neural (quasi-)metric has a total of 4 layers, with a hidden layer dimension equal to the event embedding dimensions, that is, either 2 or 4, and the last layer projects the representation to a scalar, i.e., the predicted distance. ... We train for 5,000 epochs with a learning rate of 10^-4 using the Adam W optimizer, and apply a max gradient norm of 1. |