Influence maximization in temporal networks

Abstract

Influence maximization problem is the problem of finding the node set of size k that maximizes the spread of information or disease in a given network. Solving this problem is one of the most important parts of predicting epidemic size or optimizing a viral marketing. This problem is already proved to be NP-Hard and many approximation algorithms have been proposed. Most of these approximation algorithms aim at static networks and few algorithms can be applied to temporal networks, where their network structures change dynamically as the time elapses. In this paper, we propose a new algorithm for influence maximization problem in temporal networks. Proposed algorithm is a greedy algorithm that starts with an empty node set S and adds the node n which maximizes the influence of S ∪ {n} until |S| = k. We approximate influence of node set S with a heuristic approach because calculating influence of a node set exactly is #P-Hard. Our experiments for comparing the exact solution and solution by our proposed method show that our proposed method outputs almost exact solution on small networks that the exact solution can be obtained in practical time. By another experiments on larger networks, we demonstrate that the proposed method is more effective than the conventional methods of selecting nodes in the order of centrality values and is consistently 640 times faster than greedy method using Monte-Carlo simulation without losing much accuracy.