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].
Robust Subset Selection by Greedy and Evolutionary Pareto Optimization
Authors: Chao Bian, Yawen Zhou, Chao Qian
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically compare the performance of the greedy algorithm, EPORSS and two previous algorithms, modified greedy [Hou and Clark, 2021] and SATURATE [Krause et al., 2008a], on the application of robust influence maximization. ... The experiments are performed on two real-world data sets, ego-Facebook and as-733, downloaded from https://snap.stanford.edu/data/index.html. |
| Researcher Affiliation | Academia | Chao Bian, Yawen Zhou and Chao Qian State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy Algorithm Input: all items V = {v1, v2, . . . , vn}, the objective function F = min1 i m fi, and a budget k Output: a subset of V with k items Process: 1: Let j = 0 and Xj = ; 2: while j < k do 3: Let v = arg maxv V \Xj F(Xj {v}); 4: Let Xj+1 = Xj {v }, and j = j + 1 5: end while 6: return Xk |
| Open Source Code | No | The supplementary material is available at https://arxiv.org/abs/2205.01415. |
| Open Datasets | Yes | The experiments are performed on two real-world data sets, ego-Facebook and as-733, downloaded from https://snap.stanford.edu/data/index.html. |
| Dataset Splits | No | The paper mentions using 'ego-Facebook' and 'as-733' datasets but does not provide specific details on how these datasets were split into training, validation, or test sets for reproduction, nor does it specify if predefined splits were used. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, that would be needed to replicate the experiment. |
| Experiment Setup | Yes | The number of iterations of EPORSS is set to 2ek2n as suggested by Theorem 2. ... Specifically, for each network in as-733, we set the probability pv(u, S) to min{0.1 + 0.05 |S|, 1}, i.e., the probability of activating v is 0.1 for the first try, and then the probability increases by 0.05 once a try fails. ... To estimate the influence spread σ(X) of a subset X of nodes, we simulate the diffusion process 100 times independently and use the average as an estimation. |