Triple Eagle: Simple, Fast and Practical Budget-Feasible Mechanisms
Authors: Kai Han, You Wu, He Huang, Shuang Cui
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to evaluate the empirical performance of our BFMs, and the experimental results strongly demonstrate the efficiency and effectiveness of our approach. |
| Researcher Affiliation | Academia | Kai Han School of Computer Science and Technology Soochow University, P.R.China hankai@suda.edu.cn You Wu School of Computer Science and Technology Soochow University, P.R.China 20235227129@stu.suda.edu.cn He Huang School of Computer Science and Technology Soochow University, P.R.China huangh@suda.edu.cn Shuang Cui School of Computer Science and Technology University of Science and Technology of China lakers@mail.ustc.edu.cn |
| Pseudocode | Yes | Algorithm 1: Triple Eagle Ran(α), Algorithm 2: LSDPricing(A, C, η), Algorithm 3: Triple Eagle Det(α), Algorithm 4: Triple Eagle Nm(α) |
| Open Source Code | Yes | The code of our paper can be found at: https://anonymous.4open.science/r/Triple Eagle-4D1B/README.md |
| Open Datasets | Yes | We use three real social network datasets: (1) Flixster [5] with 28,843 nodes and 272,786 edges; (2) Epinions [29] with 75,879 nodes and 508,837 edges; and (3) Slashdot [29] with 82,168 nodes 948,464 edges. [...] Following [8, 16, 20, 22], we use the CIFAR-10 dataset [8, 16, 20, 22] containing ten thousands 32 32 color images |
| Dataset Splits | No | The paper mentions using datasets like Flixster, Epinions, Slashdot, and CIFAR-10, but it does not provide specific train/validation/test splits, percentages, or explicit methodologies for splitting the data. |
| Hardware Specification | Yes | All the algorithms are implemented using C++ and are run on a Linux server with Intel Xeon Gold 6126 @ 2.60GHz CPU and 128GB memory. |
| Software Dependencies | No | The paper states "All the algorithms are implemented using C++" but does not provide specific version numbers for C++ or any other software dependencies, libraries, or frameworks used. |
| Experiment Setup | Yes | We adopt the well-known Independence Cascade (IC) model [27] for the influence spread function f( ), and follow [14] to set the activation probability pu,v of each edge (u, v) to 1/Nin(v), where Nin(v) denotes the set of in-neighbors of v. The cost c(u) of each node is generated uniformly at random from the interval [0, 1]. Each implemented randomized algorithm is independently executed 50 times, and the average result is reported. |