Abstract Influence estimation (IE) and influence maximization (IM) are among the most extensively studied problems in social network analysis. Assuming diffusion (i.e., the spread of diseases) within a social network, IE aims to estimate the influence (i.e., the number of infected nodes) for a given set of seeds; and IM aims to identify a given number of seed nodes that maximize the influence. For both IE and IM, widely-adopted strategies involve repeating Monte Carlo (MC) simulations of diffusion over and over for various seed sets, which is computationally expensive. In this work, we present Monte Carlo Simulator+ (MONSTOR+), an inductive machine learning method designed to estimate the influence of given seed-node sets in social networks under two diffusion models—the independent cascade (IC) model and the linear threshold (LT) model. Due to its inductive nature, MONSTOR+ is applicable to seed-node sets and social networks not included in the training data. MONSTOR+, with its ability to accurately estimate influence through a single forward pass, can greatly accelerate existing IM algorithms by replacing repeated MC simulations. In our experiments, MONSTOR+ exhibits high IE accuracy, achieving 0.955 or higher Pearson and Spearman correlation coefficients in unseen real-world social networks. Notably, MONSTOR+ is about 5 to 3000 times faster than repeated MC simulations with similar IE accuracy. For IM problems, IM algorithms equipped with MONSTOR+ are more accurate than state-of-the-art competitors in 81.5 and 77.8% of IM use cases under the IC model and LT model, respectively.

The success of a specific neural network architecture is closely tied to the dataset and task it tackles; there is no one-size-fits-all solution. Thus, considerable efforts have been made to quickly and accurately estimate the performances of neural architectures, without full training or evaluation, for given tasks and datasets. Neural architecture encoding has played a crucial role in the estimation, and graph-based methods, which treat an architecture as a graph, have shown prominent performance. For enhanced representation learning of neural architectures, we introduce FLOWERFORMER, a powerful graph transformer that in-corporates the information flows within a neural architecture. FLOWERFORMER consists of two key components: (a) bidirectional asynchronous message passing, inspired by the flows; (b) global attention built on flow-based masking. Our extensive experiments demonstrate the superiority of FLOWERFORMER over existing neural encoding methods, and its effectiveness extends beyond computer vision models to include graph neural networks and auto speech recognition models. Our code is available at h ttp://git hub. com/yOngjaenius/cvpR2024_FLOWERFormer.