Discovering Interesting Cycles in Directed Graphs

TL;DR

This paper introduces the problem of discovering interesting cycles in directed graphs, proposes measures for interestingness, analyzes computational complexity, and presents heuristic algorithms validated on real-world datasets.

Contribution

It formalizes the interesting cycle discovery problem, analyzes its NP-hardness, and offers heuristic algorithms with practical validation on real data.

Findings

Interestingness measure correlates with cycle significance.

Finding interesting cycles is NP-hard and hard to approximate.

Heuristic algorithms are effective on real-world datasets.

Abstract

Cycles in graphs often signify interesting processes. For example, cyclic trading patterns can indicate inefficiencies or economic dependencies in trade networks, cycles in food webs can identify fragile dependencies in ecosystems, and cycles in financial transaction networks can be an indication of money laundering. Identifying such interesting cycles, which can also be constrained to contain a given set of query nodes, although not extensively studied, is thus a problem of considerable importance. In this paper, we introduce the problem of discovering interesting cycles in graphs. We first address the problem of quantifying the extent to which a given cycle is interesting for a particular analyst. We then show that finding cycles according to this interestingness measure is related to the longest cycle and maximum mean-weight cycle problems (in the unconstrained setting) and to the maximum Steiner cycle and maximum mean Steiner cycle problems (in the constrained setting). A complexity analysis shows that finding interesting cycles is NP-hard, and is NP-hard to approximate within a constant factor in the unconstrained setting, and within a factor polynomial in the input size for the constrained setting. The latter inapproximability result implies a similar result for the maximum Steiner cycle and maximum mean Steiner cycle problems. Motivated by these hardness results, we propose a number of efficient heuristic algorithms. We verify the effectiveness of the proposed methods and demonstrate their practical utility on two real-world use cases: a food web and an international trade-network dataset.