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

TL;DR

MANCaLog is a logic-based formalism designed to model complex cascade processes in multi-attribute networks, capturing attributes, temporal dynamics, uncertainty, and competing cascades, with proven computational tractability.

Contribution

This paper introduces MANCaLog, a novel logic programming framework that satisfies seven key properties for modeling multi-attribute network cascades, filling a gap in existing formal methods.

Findings

MANCaLog can represent attributes, time, uncertainty, and non-monotonic diffusion.

Algorithms for minimal model computation are developed.

The formalism is applicable to real-world cascade scenarios.

Abstract

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.