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.
Inferring Graphs from Cascades: A Sparse Recovery Framework
Authors: Jean Pouget-Abadie, Thibaut Horel
ICML 2015 | Venue PDF | LLM Run Details | Input Tokens: 16,948 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,153 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 | Finally we prove an almost matching lower bound of O(s log m s ) and validate our approach empirically on synthetic graphs. |
| Researcher Affiliation | Academia | Jean Pouget-Abadie EMAIL Harvard University Thibaut Horel EMAIL Harvard University |
| Pseudocode | No | The paper provides mathematical formulations and descriptions of algorithms, but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is made available. |
| Open Datasets | No | The paper evaluates performance on "synthetic graphs" and states "For every reported data point, we sample edge weights and generate n cascades". It does not use or provide access to a publicly available or open dataset. |
| Dataset Splits | No | The paper does not specify training, validation, or test dataset splits. It describes generating cascades on synthetic graphs. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | For every reported data point, we sample edge weights and generate n cascades from the (IC) model for n {100, 500, 1000, 2000, 5000}. We compare for each algorithm the estimated graph Gˆ with G. The initial probability of a node being a source is fixed to 0.05, i.e. an average of 15 nodes source nodes per cascades for all experiments, except for Figure (f). All edge weights are chosen uniformly in the interval [0.2, 0.7]... The parameter λ is chosen to be of the order O( p log m/(αn)). |