Towards Generating Explanations for ASP-Based LinkAnalysis using Declarative Program Transformations

The explication and the generation ofexplanationsare prominent top-ics in artificial intelligence and data science, in order to make methods and sys-tems more transparent and understandable for humans.This paper investigates the problem oflink analysis, specifically link predictionand anomalous link discovery insocial networksusing the declarative methodof Answer set programming (ASP). Applying ASP for link prediction providesa powerful declarative approach, e. g., for incorporating domain knowledge forexplicative prediction. In this context, we propose a novel method for generatingexplanations – as offline justifications – using declarative program transforma-tions. The method itself is purely based on syntactic transformations of declara-tive programs, e. g., in an ASP formalism, using rule instrumentation.We demonstrate the efficacy of the proposed approach, exemplifying it in an ap-plication on link analysis in social networks, also including domain knowledge