Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Optimal Inference in Contextual Stochastic Block Models
Authors: O Duranthon, Lenka Zdeborova
TMLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 22,311 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,133 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that there can be a considerable gap between the accuracy reached by this algorithm and the performance of the GNN architectures proposed in the literature. This suggests that the CSBM, along with the comparison to the performance of the optimal algorithm, readily accessible via our implementation, can be instrumental in the development of more performant GNN architectures. |
| Researcher Affiliation | Academia | O. Duranthon and L. Zdeborová Statistical Physics of Computation laboratory, École polytechnique fédérale de Lausanne (EPFL), Switzerland EMAIL |
| Pseudocode | Yes | Algorithm 1: The AMP BP algorithm. Algorithm 2: The AMP AMP algorithm. Algorithm 3: The multi-community AMP BP algorithm. |
| Open Source Code | Yes | We provide a simple-to-use implementation of the algorithm (attached in the Supplementary Material) and in our repository.1 1gitlab.epfl.ch/spoc-idephics/csbm |
| Open Datasets | No | Our motivation to study the CSBM is due to the interest this model has recently received in the community developing and analyzing graph neural networks (GNNs). Indeed, this model provides an idealized synthetic dataset on which graph neural networks can be conveniently evaluated and benchmarked. |
| Dataset Splits | Yes | We define Ξ as the set of revealed training nodes, that are observed. We set ρ = |Ξ|/N; ρ = 0 for unsupervised learning. We assume Ξ is drawn independently with respect to the group memberships. We define PU,i an additional node-dependant prior. It is used to inject information about the memberships of the observed nodes: We consider the same task as before: on a single instance of CSBM a fraction ρ of node labels are revealed and the GNN must guess the hidden labels. |
| Hardware Specification | Yes | Computationally, running the code for a single experiment, N = 3 × 10^4, α = 1 and d = 5 takes around one minute on one CPU core. |
| Software Dependencies | No | We use the architectures implemented by the Graph Gym package (You et al., 2020). |
| Experiment Setup | Yes | Intra-layer parameters: we take the internal dimension h = 64; we use batch normalization; no dropout; PReLU activation; add aggregation; convolution operation in {generalconv, gcnconv, gatconv} (as defined in the config.py file). Inter-layer design: we take K ∈ {1, 2, 3, 4} layers of message-passing; no pre-process layer; one postprocess layer; stack connection. Training configuration: The batch size is one, we train on the entire graph, revealing a proportion ρ of labels; the learning rate is 3 × 10^−3; we train for forty epochs with Adam; weight decay is 5 × 10^−4. |