CELF++: optimizing the greedy algorithm for influence maximization in social networks

By Amit Goyal, Wei Lu, Laks V. S. Lakshmanan. The Web Conference, 2011

962 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

View Paper: https://www.semanticscholar.org/paper/4b983a1a0b370a4ea87c2be4f2c7c53b13edb4f6

How researchers see this paper

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

In Artificial Intelligence

1. Tao Zhou
2. Xiaokui Xiao
3. Youze Tang
4. Yanchen Shi
5. Linyuan Lu
6. Kyomin Jung
7. Wooram Heo
8. Yi-Cheng Zhang

In Computer Networks and Communications

1. Youze Tang
2. Yanchen Shi
3. Xiaokui Xiao
4. Kian-Lee Tan
5. Ju Fan
6. Yuchen Li
7. Yanhao Wang
8. Kyomin Jung

In Signal Processing

1. Youze Tang
2. Yanchen Shi
3. Xiaokui Xiao
4. Kyomin Jung
5. Wooram Heo
6. Amit Goyal
7. Laks V. S. Lakshmanan
8. Wei Lu

Citations in the corpus

• Influence Maximization in Near-Linear Time: A Martingale Approach (2015, methodology): “In particular, methods [5,8,20,25,29] that provide (1− 1/e− ε)-approximation solutions often require days to process small social networks with several thousands of nodes and edges; on the other hand, techniques that offer empirical efficiency rely on heuristics, due to which they are unable to offe”
• Influence Maximization on Social Graphs: A Survey (2018, methodology): “CELF++ [56] improves over CELF by further avoiding unnecessary MC simulations.”
• Vital nodes identification in complex networks (2016, methodology): “As reported in [240], for some specific network instances, the CELF algorithm is 700 times faster than the original greedy algorithm (see also [241] for a further extension of CELF, named CELF++).”
• Influence maximization: near-optimal time complexity meets practical efficiency (2014, methodology): “We adopt the C++ implementation of CELF++ made available by Goyal et al. [12], and we implement RIS and our solutions in C++. Note that RIS is designed under the IC model only, but we incorporate the techniques in Section 4.2 into RIS and extend it to the LT model.”
• IRIE: Scalable and Robust Influence Maximization in Social Networks (2011, methodology): “To estimate influence function σ(S) efficiently, Monte-Carlo simulation and other heuristics have been used in various previous works [1], [5], [7], [6], [3], [12], [8], [7], [16].”
• SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, methodology): “The authors of [13] report that CELF++ is found to be approximately 35-55% faster than CELF.”