Maximizing Influence in an Unknown Social Network
Authors: Bryan Wilder, Nicole Immorlica, Eric Rice, Milind Tambe
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | 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 {bwilder, ericr, tambe}@usc.edu 3Microsoft Research, New England nicimm@gmail.com |
| 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. |