Trinh Pham

Social network simulation enables controlled study of information dynamics and collective behavior on social graphs, yet the literature remains fragmented across disciplines. This survey provides a unified treatment of the field, organizing 97 papers from the past seven decades into five paradigms: network structure models, information diffusion, opinion dynamics, rule-based agentbased models, and large language model (LLM)-based agentic simulation. We bridge classical and generative approaches by mapping the progression from analytical update rules to language-grounded agents with semantic memory and platform-native actions, and present the first systematic architectural comparison of LLM-based simulation systems. We analyze the literature across five application domains—misinformation, political discourse, platform design, information diffusion, and user behavior—tracing a shift from descriptive analysis toward prescriptive policy prototyping. We outline seven research frontiers spanning representational alignment, multi-level validation, scalability, and responsible use. An accompanying online repository at https://github.com/tamlhp/awesome-sns catalogs 48 implementations (42 open-source), 21 benchmarks and datasets, and 22 evaluation metrics.

Time series analysis (TSA)–forecasting, anomaly detection, imputation, classification, and unified multi-task analytics– has become a battleground for sequence-modeling backbones, where Transformers, MLPs, convolutions, and state-space models compete on long context benchmarks. Since the 2023 release of Mamba, its linear time recurrence and input-dependent selectivity have triggered a surge of Mamba-based time-series models that report strong numbers yet resist like-for-like comparison. This survey provides the first focused treatment of Mamba for time series analysis, organized around four orthogonal perspectives: Model, Task, Data, and Application. The Model perspective formalizes a five-axis design space– tokenization, channel strategy, directional scan, hybridization, and decomposition– together with a three-pattern architectural toolbox of pure, bidirectional, and hybrid designs. The Task perspective re-indexes the corpus across the five canonical TSA tasks. The Data perspective re-tags it by data shape, spanning univariate, multivariate, spatio-temporal graph, and irregular series. The Application perspective then surveys deployments in healthcare, energy, traffic, climate, finance, activity recognition, and foundation-scale settings. Building on these views, we distill practical guidelines for variant selection, training recipes, and Mamba-specific pitfalls. We also catalog public implementations, datasets, and metrics, and we map out open frontiers in gain attribution, modeling regimes, and cross-task unification. An online repository is maintained at https://github.com/tamlhp/awesome-mamba-ts.