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The Influence Maximization (IM) problem aims at finding k seed vertices in anetwork, starting from which influence can be spread in the network to themaximum extent. In this paper, we propose QuickIM, the first versatile IMalgorithm that attains all the desirable properties of a practically applicableIM algorithm at the same time, namely high time efficiency, good resultquality, low memory footprint, and high robustness. On real-world socialnetworks, QuickIM achieves the $\Omega(n + m)$ lower bound on time complexityand $\Omega(n)$ space complexity, where $n$ and $m$ are the number of verticesand edges in the network, respectively. Our experimental evaluation verifiesthe superiority of QuickIM. Firstly, QuickIM runs 1-3 orders of magnitudefaster than the state-of-the-art IM algorithms. Secondly, except EasyIM,QuickIM requires 1-2 orders of magnitude less memory than the state-of-the-artalgorithms. Thirdly, QuickIM always produces as good quality results as thestate-of-the-art algorithms. Lastly, the time and the memory performance ofQuickIM is independent of influence probabilities. On the largest network usedin the experiments that contains more than 3.6 billion edges, QuickIM is ableto find hundreds of influential seeds in less than 4 minutes, while all thestate-of-the-art algorithms fail to terminate in an hour.

QuickIM: Efficient, Accurate and Robust Influence Maximization Algorithm on Billion-Scale Networks