Maximum coverage and maximum connected covering in social networks with partial topology information

Abstract

Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. There is a related problem when the recruited nodes are connected. It is called the maximum connected cover problem. This problem ensures a strong coordination between the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. In this work, we propose heuristics to the maximum connected cover problem and the maximum coverage problem with different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms.

Socio-spatial influence maximization in location-based social networks

“…For instance, Jiang et al [16] developed a simulated annealing algorithm and introduced two new heuristic methods in order to accelerate the convergence while searching for influential nodes. Reyes and Silva [17] search for influential nodes in the form of a maximum coverage problem and presented some new heuristics that take into account the network topology. Li et al [18] suggested a new formulation of the problem, so-called the Keyword-Based Targeted Influence Maximization (KB-TIM).…”