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 [1].
Random-Radius Ball Method for Estimating Closeness Centrality
Authors: Wataru Inariba, Takuya Akiba, Yuichi Yoshida
AAAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of the RRB method over existing algorithms is demonstrated through experiments on real-world networks. |
| Researcher Affiliation | Collaboration | Wataru Inariba The University of Tokyo and JST, ERATO, Kawarabayashi Large Graph Project EMAIL Takuya Akiba Preferred Networks, Inc. EMAIL Yuichi Yoshida National Institute of Informatics and Preferred Infrastructure, Inc. EMAIL |
| Pseudocode | Yes | Algorithm 1: Random-radius ball (RRB) method. Algorithm 2: Random-radius ball (RRB) method with bootstrapping. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of the RRB method code. It mentions using a third-party framework: "For HB, we used the Web Graph framework, which is the Java program provided by the authors (Boldi and Vigna 2004)." |
| Open Datasets | Yes | Networks. All of the networks were collected from the Stanford Large Network Dataset Collection (Leskovec and Krevl 2014) and Laboratory for Web Algorithms (Boldi and Vigna 2004; Boldi et al. 2011). |
| Dataset Splits | No | The paper does not explicitly state the training, validation, or test dataset splits. It mentions using various networks for experiments, but no details on how these were partitioned for evaluation. |
| Hardware Specification | Yes | All of the experiments were conducted on a machine with two Intel Xeon E5540 processors and 48 Gi B of main memory. |
| Software Dependencies | Yes | We implemented RRB, RRB-BS, and ADS in C++11 and compiled them with gcc 4.8.2. For HB, we used the Web Graph framework, which is the Java program provided by the authors (Boldi and Vigna 2004). |
| Experiment Setup | Yes | For RRB-BS, we set s = 3. For all of these methods, we can control the trade-off between the time complexity and accuracy by varying select parameters. Throughout all of the experiments, we estimated the harmonic centrality of the vertices, i.e., the distance-decay function α(x) was set to 1/x. To evaluate the (normalized) RMSE of an estimate, we ran an algorithm 100 times with different random seeds and then took the average. |