The Solution Distribution of Influence Maximization: A High-level Experimental Study on Three Algorithmic Approaches

Abstract

Influence maximization is among the most fundamental algorithmic problems in social influence analysis. Over the last decade, a great effort has been devoted to developing efficient algorithms for influence maximization, so that identifying the ``best'' algorithm has become a demanding task. In SIGMOD'17, Arora, Galhotra, and Ranu reported benchmark results on eleven existing algorithms and demonstrated that there is no single state-of-the-art offering the best trade-off between computational efficiency and solution quality. In this paper, we report a high-level experimental study on three well-established algorithmic approaches for influence maximization, referred to as Oneshot, Snapshot, and Reverse Influence Sampling (RIS). Different from Arora et al., our experimental methodology is so designed that we examine the distribution of random solutions, characterize the relation between the sample number and the actual solution quality, and avoid implementation dependencies. Our main findings are as follows: 1. For a sufficiently large sample number, we obtain a unique solution regardless of algorithms. 2. The average solution quality of Oneshot, Snapshot, and RIS improves at the same rate up to scaling of sample number. 3. Oneshot requires more samples than Snapshot, and Snapshot requires fewer but larger samples than RIS. We discuss the time efficiency when conditioning Oneshot, Snapshot, and RIS to be of identical accuracy. Our conclusion is that Oneshot is suitable only if the size of available memory is limited, and RIS is more efficient than Snapshot for large networks; Snapshot is preferable for small, low-probability networks.