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The explication and the generation of explanations are prominent topics inartificial intelligence and data science, in order to make methods and systemsmore transparent and understandable for humans. This paper investigates theproblem of link analysis, specifically link prediction and anomalous linkdiscovery in social networks using the declarative method of Answer setprogramming (ASP). Applying ASP for link prediction provides a powerfuldeclarative approach, e.g., for incorporating domain knowledge for explicativeprediction. In this context, we propose a novel method for generatingexplanations - as offline justifications - using declarative programtransformations. The method itself is purely based on syntactic transformationsof declarative programs, e.g., in an ASP formalism, using rule instrumentation.We demonstrate the efficacy of the proposed approach, exemplifying it in anapplication on link analysis in social networks, also including domainknowledge.

Towards Generating Explanations for ASP-Based Link Analysis using Declarative Program Transformations