The Importance of Communities for Learning to Influence
Authors: Eric Balkanski, Nicole Immorlica, Yaron Singer
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the performance of COPS and three other algorithms on real and synthetic networks. We show that COPS performs well in practice, it outperforms the previous optimization from samples algorithm and gets closer to the solution obtained when given complete access to the influence function. |
| Researcher Affiliation | Collaboration | Eric Balkanski Harvard University ericbalkanski@g.harvard.edu Nicole Immorlica Microsoft Research nicimm@microsoft.com Yaron Singer Harvard University yaron@seas.harvard.edu |
| Pseudocode | Yes | Algorithm 1 COPS, learns to influence networks with COmmunity Pruning from Samples. Algorithm 2 OVERLAP(a, b, ), returns true if a and b have marginal contributions that overlap by at least a factor . |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The second is a subgraph of the DBLP co-authorship network, which has ground truth communities as described in [LK15], where nodes of degree at most 10 were pruned to obtain n = 54k, m = 361k and where the 1.2k nodes with degree at least 50 were considered as potential nodes in the solution. [LK15] Jure Leskovec and Andrej Krevl. Snap datasets, stanford large network dataset collection. 2015. |
| Dataset Splits | No | The paper does not specify exact training/validation/test splits (e.g., percentages or sample counts). It mentions samples are drawn from a product distribution but not how data is partitioned for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific computing environments) used for running its experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) that would be needed to reproduce the experiment. |
| Experiment Setup | No | The paper states, "We further describe the parameters of each plot in Appendix F.", but Appendix F is not included in the provided text. Therefore, specific experimental setup details like hyperparameters or training configurations are not present in the main text. |