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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
Authors: Zheng Wen, Branislav Kveton, Michal Valko, Sharan Vaswani
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that in several representative graph topologies, the regret of IMLin UCB scales as suggested by our upper bounds. Our experiments also show that IMLin UCB with linear generalization can lead to low regret in real-world online influence maximization. |
| Researcher Affiliation | Collaboration | Zheng Wen Adobe Research EMAIL Branislav Kveton Adobe Research EMAIL Michal Valko Seque L team, INRIA Lille Nord Europe EMAIL Sharan Vaswani University of British Columbia EMAIL |
| Pseudocode | Yes | Algorithm 1 IMLin UCB: Influence Maximization Linear UCB |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | Yes | Specifically, we compare IMLin UCB with CUCB in a subgraph of Facebook network from [22]. [22] Jure Leskovec and Andrej Krevl. Snap datasets: Stanford large network dataset collection. http://snap.stanford.edu/data, jun 2014. |
| Dataset Splits | No | The paper mentions using a 'subgraph of Facebook network' but does not specify exact train/validation/test dataset splits (e.g., percentages or counts) or reference standard predefined splits with specific details for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'node2vec algorithm [15]' and 'offline IM algorithm proposed in [27]' but does not provide specific version numbers for these or any other software dependencies needed for replication. |
| Experiment Setup | Yes | We set n = 5000 and K = 10 in this experiment. For IMLin UCB, we choose d = 10 and generate edge feature xe s as follows: we first use node2vec algorithm [15] to generate a node feature in ℜd for each node v V; then for each edge e, we generate xe as the element-wise product of node features of the two nodes connected to e. ... For both CUCB and IMLin UCB, we choose ORACLE as the state-of-the-art offline IM algorithm proposed in [27]. |