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].
Maximizing Influence in an Unknown Social Network
Authors: Bryan Wilder, Nicole Immorlica, Eric Rice, Milind Tambe
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present the ARISEN algorithm, which leverages community structure to find an influential seed set. Experiments on real world networks of homeless youth, village populations in India, and others demonstrate ARISEN s strong empirical performance. To formally demonstrate how ARISEN exploits community structure, we prove an approximation guarantee for ARISEN on graphs drawn from the Stochastic Block Model. |
| Researcher Affiliation | Collaboration | Bryan Wilder,12 Nicole Immorlica,3 Eric Rice,24 Milind Tambe12 1Department of Computer Science, 2Center for Artificial Intelligence in Society 4School of Social Work University of Southern California EMAIL 3Microsoft Research, New England EMAIL |
| Pseudocode | Yes | Algorithm 3 ARISEN(R, T, B) ... Algorithm 1 Initialize Weights(H, K, R, T, B) ... Algorithm 2 Refine Weights(w, H) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability. |
| Open Datasets | Yes | The first network is homeless: Two networks (a and b) gathered from home- less youth in Los Angeles and used to study HIV prevention with 150-200 nodes each. Second, india: Three networks of the household-level social contacts of villages in rural India. Gathered by Banerjee et al. (2014) to study diffusion of information about microfinance programs, with 250-350 nodes each. Third, netscience1: a collaboration network of network science researchers with 1461 nodes. Fourth, SBM: a synthetic SBM graphs with 1000 nodes. |
| Dataset Splits | No | The paper mentions evaluating on datasets but does not explicitly detail training, validation, and test splits (e.g., specific percentages or sample counts). It refers to |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments. It focuses on the algorithm and empirical performance without detailing the computational environment. |
| Software Dependencies | No | The paper mentions using TIM (Tang, Xiao, and Shi 2014) as a baseline algorithm, but it does not specify software dependencies like programming languages, libraries, or their version numbers for reproducibility. |
| Experiment Setup | No | The paper describes the parameters for ARISEN (R, T, K, B) and the Independent Cascade Model (q) but does not provide details of hyperparameter tuning, optimization settings, or other system-level training configurations typically found in an 'experimental setup' section for deep learning or complex models. For example, it states 'Section 6 gives settings which obtain theoretical guarantees' for R and T, but does not explicitly list the values for experiments. |