Reasoning about Complex Networks: A Logic Programming Approach

TL;DR

This paper introduces MANCaLog, a logic programming formalism for reasoning about complex networks, demonstrated through group membership analysis in social networks, including criminal gangs, with experimental validation.

Contribution

The paper presents MANCaLog, a novel formalism satisfying all previous desiderata for reasoning in complex networks, along with algorithms and a prototype for real-world applications.

Findings

MANCaLog effectively models group membership in social networks.

Experimental results on real datasets validate the approach.

Enhanced understanding of criminal networks through degree of membership analysis.

Abstract

Reasoning about 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 present the MANCaLog language, a formalism based on logic programming that satisfies a set of desiderata proposed in previous work as recommendations for the development of approaches to reasoning in complex networks. To the best of our knowledge, this is the first formalism that satisfies all such criteria. We first focus on algorithms for finding minimal models (on which multi-attribute analysis can be done), and then on how this formalism can be applied in certain real world scenarios. Towards this end, we study the problem of deciding group membership in social networks: given a social network and a set of groups where group membership of only some of the individuals in the network is known, we wish to determine a degree of membership for the remaining group-individual pairs. We develop a prototype implementation that we use to obtain experimental results on two real world datasets, including a current social network of criminal gangs in a major U.S.\ city. We then show how the assignment of degree of membership to nodes in this case allows for a better understanding of the criminal gang problem when combined with other social network mining techniques -- including detection of sub-groups and identification of core group members -- which would not be possible without further identification of additional group members.