Robust Sequence Networked Submodular Maximization

TL;DR

This paper introduces a novel robust greedy algorithm for sequence networked submodular maximization, addressing the challenge of element removal impacts in networked sequences, with proven approximation guarantees and real-world application validation.

Contribution

It is the first to study the RoseNets problem, integrating robust optimization with sequence networked submodular maximization and providing an effective greedy solution.

Findings

The proposed algorithm is robust against arbitrary element removal.

Approximation ratio depends on the number of removed elements and network topology.

Experimental results validate the algorithm's effectiveness in recommendation and link prediction tasks.

Abstract

In this paper, we study the \underline{R}obust \underline{o}ptimization for \underline{se}quence \underline{Net}worked \underline{s}ubmodular maximization (RoseNets) problem. We interweave the robust optimization with the sequence networked submodular maximization. The elements are connected by a directed acyclic graph and the objective function is not submodular on the elements but on the edges in the graph. Under such networked submodular scenario, the impact of removing an element from a sequence depends both on its position in the sequence and in the network. This makes the existing robust algorithms inapplicable. In this paper, we take the first step to study the RoseNets problem. We design a robust greedy algorithm, which is robust against the removal of an arbitrary subset of the selected elements. The approximation ratio of the algorithm depends both on the number of the removed elements and the network topology. We further conduct experiments on real applications of recommendation and link prediction. The experimental results demonstrate the effectiveness of the proposed algorithm.