Structured Prediction of Network Response
Authors: Hongyu Su, Aristides Gionis, Juho Rousu
ICML 2014 | Conference PDF | Archive PDF | Plain Text | 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 HONGYU.SU@AALTO.FI Aristides Gionis ARISTIDES.GIONIS@AALTO.FI Juho Rousu JUHO.ROUSU@AALTO.FI 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). |