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..
Structured Prediction of Network Response
Authors: Hongyu Su, Aristides Gionis, Juho Rousu
ICML 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we demonstrate that taking advantage of the context given by the actions and the network structure leads SPIN to a markedly better predictive performance over competing methods. In this section, we evaluate the performance of SPIN and compare it with the state-of-the-art methods through extensive experiments. We use two real-world datasets, DBLP and Memetracker, described below. Statistics of the datasets are given in Table 1. |
| Researcher Affiliation | Academia | Hongyu Su EMAIL Aristides Gionis EMAIL Juho Rousu EMAIL Helsinki Institute for Information Technology (HIIT) Department of Information and Computer Science, Aalto University, Finland |
| Pseudocode | No | The paper describes the steps of the GREEDY algorithm in paragraph text (e.g., 'The algorithm starts with an activated vertex set...'), but it does not provide a formal pseudocode block or a clearly labeled algorithm. |
| Open Source Code | No | The paper mentions that the implementation for the ICM-EM algorithm is publicly available (footnote 3), but it does not provide access to the source code for their proposed SPIN method. |
| Open Datasets | Yes | We use two real-world datasets, DBLP and Memetracker, described below. Statistics of the datasets are given in Table 1. DBLP1 dataset is a collection of bibliographic information on major computer science journals and proceedings. 1http://www.informatik.uni-trier.de/ ley/ db/ Memetracker2 dataset is a set of phrases propagated over prominent online news sites in March 2009. 2http://Memetracker.org |
| Dataset Splits | Yes | The experimental results are from a five-fold cross validation. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud resources). |
| Software Dependencies | No | The paper mentions several algorithms and tools (e.g., LDA algorithm, ICM-EM, CPLEX) but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper mentions some parameters like the regularization slack parameter C and scaling factors λ and β for loss functions. However, it does not provide concrete hyperparameter values or detailed system-level training settings for their primary experiments in the main text (e.g., specific C value, learning rates, batch sizes, number of epochs, or default λ and R values used for the main comparisons). |