notoliq

Notoliq is a theoretical framework used in information science and network theory to study how ideas and behaviors spread through social systems. It provides a simplified, modular model for simulating diffusion processes in online and offline networks. The framework is characterized by two coupled layers: a diffusion layer that represents agents with varying receptivity to content, and a content layer that encodes topics with different appeal and resistance to adoption. Nodes carry state variables such as adoption status and attention level, while edges carry weights reflecting contact strength and trust.

In Notoliq, adoption occurs probabilistically based on neighbor influence, topic affinity, and time-decay of attention. The model supports extensions that include heterogeneous agents, skepticism, competing contents, and external shocks. Variants such as Notoliq-Core and Notoliq-Extended are used to study different regimes, from rapid cascades to slow, persistent diffusion.

Applications include analyzing misinformation dynamics, forecasting the reach of marketing campaigns, and designing interventions to curb harmful spread. The framework is valued for its tractability, as well as its flexibility to incorporate qualitative factors alongside quantitative signals.

Limitations exist, including sensitivity to parameter choices, simplified agent behavior, and challenges in aligning simulations with real-world data. As a theoretical tool, Notoliq aims to illuminate fundamental mechanisms of information diffusion rather than provide precise predictions.

Notoliq has appeared in academic exercises and teaching materials to illustrate diffusion concepts, and it remains a basis for discussions about how network structure and content properties interact to shape propagation.