# MANCaLog: A Logic for Multi-Attribute Network Cascades (Technical Report)

arXiv: https://arxiv.org/abs/1301.0302 · HTML: https://mlsanity.com/paper/1301.0302/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jan 2013.

## Built on by (most cited first, 5 of 5)

- [PyReason: Software for Open World Temporal Logic](https://mlsanity.com/paper/2302.13482/) (2023)
- [Extensions to Generalized Annotated Logic and an Equivalent Neural Architecture](https://mlsanity.com/paper/2302.12195/) (2023)
- [Reasoning about Complex Networks: A Logic Programming Approach](https://mlsanity.com/paper/2209.15067/) (2022)
- [Scalable Semantic Non-Markovian Simulation Proxy for Reinforcement Learning](https://mlsanity.com/paper/2310.06835/) (2023)
- [Lattice Annotated Temporal (LAT) Logic for Non-Markovian Reasoning](https://mlsanity.com/paper/2509.02958/) (2025)

## Abstract (arXiv)

The modeling of cascade processes in multi-agent systems in the form of complex networks has in recent years become an important topic of study due to its many applications: the adoption of commercial products, spread of disease, the diffusion of an idea, etc. In this paper, we begin by identifying a desiderata of seven properties that a framework for modeling such processes should satisfy: the ability to represent attributes of both nodes and edges, an explicit representation of time, the ability to represent non-Markovian temporal relationships, representation of uncertain information, the ability to represent competing cascades, allowance of non-monotonic diffusion, and computational tractability. We then present the MANCaLog language, a formalism based on logic programming that satisfies all these desiderata, and focus on algorithms for finding minimal models (from which the outcome of cascades can be obtained) as well as how this formalism can be applied in real world scenarios. We are not aware of any other formalism in the literature that meets all of the above requirements.