Towards Effective Planning Strategies for Dynamic Opinion Networks
Authors: Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, Michael Huhns
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. |
| Researcher Affiliation | Academia | Bharath Muppasani, Protik Nag, Vignesh Narayanan, Biplav Srivastava, and Michael N. Huhns AI Institute and Department of Computer Science University of South Carolina, USA {bharath@email., pnag@email., vignar@, biplav.s@, huhns@}sc.edu |
| Pseudocode | Yes | A pseudocode for this ranking algorithm is presented in Algorithm 1 in Appendix A.5. |
| Open Source Code | Yes | The code and the datasets developed as part of the analysis presented in this paper can be found in [33]. [33] B. Muppasani, P. Nag, V. Narayanan, B. Srivastava, and M. N. Huhns. Code and datasets for the paper, 2024. Available at: https://github.com/ai4society/InfoSpread-NeurIPS-24. |
| Open Datasets | No | The datasets used in related works, such as [17], typically consist of network structures, and no real-time opinion propagation data could be found. Therefore, to evaluate our intervention strategies, we generated two sets of synthetic datasets using the Watts-Strogatz model with the training dataset s configurations. |
| Dataset Splits | No | The model parameters yielding the best performance on the validation set are preserved for subsequent evaluation phases. |
| Hardware Specification | Yes | We have used two servers to run our experiments. One with 48-core nodes each hosting 2 V100 32G GPUs and 128GB of RAM. Another with 256-cores, eight A100 40GB GPUs, and 1TB of RAM. The processor speed is 2.8 GHz. |
| Software Dependencies | No | The development of our supervised learning models, particularly those utilizing graph convolutional networks, leveraged several Python packages instrumental in defining, training, and evaluating our models: torch, torch_geometric, networkx. ... The implementation of our Res Net model and the training process was facilitated by the following Python packages: torch, torch.nn, torch.nn.functional, torch.optim. |
| Experiment Setup | Yes | Our SL setup is coupled with a ranking algorithm which is shown in Algorithm 1. We GCN with an input size of 3 (opinion value, degree of node, proximity to source node), a hidden size of 128, and an output size of 1. The model was trained using the Adam optimizer with a learning rate of 0.001 and a binary cross-entropy loss function. The training process involved 1000 epochs, where in each epoch, a graph with 25 nodes was generated. ... The Neural Network model is trained using a variant of Q-learning... The learning rate is set to 5 10^-4, and mean squared error (MSE) loss is utilized... We have used a batch-size of 100 across the experiments. The policy network parameters are optimized using the Adam optimizer, and the target network s parameters are periodically updated to reflect the policy network, reducing the likelihood of divergence. The training process continues for 300 number of episodes... |