Information Gathering in Networks via Active Exploration
Authors: Adish Singla, Eric Horvitz, Pushmeet Kohli, Ryen White, Andreas Krause
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our methodology on various simulated problem instances as well as on data collected from social question answering system deployed within a large enterprise. |
| Researcher Affiliation | Collaboration | Adish Singla ETH Zurich adish.singla@inf.ethz.ch Eric Horvitz Microsoft Research horvitz@microsoft.com Pushmeet Kohli Microsoft Research pkohli@microsoft.com Ryen White Microsoft Research ryen.white@microsoft.com Andreas Krause ETH Zurich krausea@ethz.ch |
| Pseudocode | Yes | Algorithm 1: Algorithm NETEXP |
| Open Source Code | No | The paper does not provide a direct link to open-source code for the methodology described. |
| Open Datasets | No | The paper mentions generating synthetic datasets ('Erd os-R enyi random graph', 'Preferential attachment graph') and using a 'real-world social Q&A system' dataset, but it does not provide concrete access information (link, DOI, repository, formal citation with authors/year) for any publicly available or open dataset. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or citations to predefined splits) for training, validation, or testing data needed to reproduce the partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | We considered a realistic setting of ldeg = 1 and lval = 1 visibility. For running different algorithms, we considered value quota Q = 1, with tolerance of β = 0.05. We used ϵ = 0.5 for NETEXP across all of the datasets without any further tuning. |