Influence maximization: near-optimal time complexity meets practical efficiency

By Youze Tang, Xiaokui Xiao, Yanchen Shi. SIGMOD Conference, 2014

900 citations recorded by the source corpus.

Given a social network G and a constant $k$, the influence maximization problem asks for k nodes in G that (directly and indirectly) influence the largest number of nodes under a pre-defined diffusion model. This problem finds important applications in viral marketing, and has been extensively studied in the literature. Existing algorithms for influence maximization, however, either trade approximation guarantees for practical efficiency, or vice versa. In particular, among the algorithms that achieve constant factor approximations under the prominent independent cascade (IC) model or linear threshold (LT) model, none can handle a million-node graph without incurring prohibitive overheads. This paper presents TIM, an algorithm that aims to bridge the theory and practice in influence maximization. On the theory side, we show that TIM runs in O((k+ l) (n+m) log n/ε2) expected time and returns a (1-1/e-ε)-approximate solution with at least 1 - n-l probability. The time complexity of TIM is near-optimal under the IC model, as it is only a log n factor larger than the Ω(m + n) lower-bound established in previous work (for fixed k, l, and ε). Moreover, TIM supports the triggering model, which is a general diffusion model that includes both IC and LT as special cases. On the practice side, TIM incorporates novel heuristics that significantly improve its empirical efficiency without compromising its asymptotic performance. We experimentally evaluate TIM with the largest datasets ever tested in the literature, and show that it outperforms the state-of-the-art solutions (with approximation guarantees) by up to four orders of magnitude in terms of running time. In particular, when k = 50, ε = 0.2, and l = 1, TIM requires less than one hour on a commodity machine to process a network with 41.6 million nodes and 1.4 billion edges. This demonstrates that influence maximization algorithms can be made practical while still offering strong theoretical guarantees.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/abb152802d5b4686a394e221abe951187ea06158

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Science and Operations Research

1. My T. Thai
2. Thang N. Dinh
3. Hung T. Nguyen
4. Youze Tang
5. Yanchen Shi
6. Xiaokui Xiao
7. Jian Pei
8. Kian-Lee Tan

In Statistical and Nonlinear Physics

1. My T. Thai
2. Thang N. Dinh
3. Hung T. Nguyen
4. Kian-Lee Tan
5. Ju Fan
6. Yuchen Li
7. Yanhao Wang
8. Youze Tang

Citations in the corpus

• Influence Maximization in Near-Linear Time: A Martingale Approach (2015, methodology): “In addition, the same assumption is implicitly adopted in previous work [33].”
• When Social Influence Meets Item Inference (2015, background): “According to the above study, it is assumed that the number of activations of a hyperedge e follows the Poisson distribution to handle the social influence and item inference jointly.”
• Identifying a set of influential spreaders in complex networks (2016, methodology): “Tang et al. 32 presented a Two-phase Influence Maximization (TIM) algorithm that aimed to bridge the theory and practice in influence maximization.”
• Influence Maximization on Social Graphs: A Survey (2018, methodology): “By improving the parameter estimation procedure, they also propose the TIM+ algorithm in [15].”
• Stop-and-Stare: Optimal Sampling Algorithms for Viral Marketing in Billion-scale Networks (2016, methodology): “Viral Marketing, in which brand-awareness information is widely spread via the word-of-mouth effect, has emerged as one of the most effective marketing channels.”