This is a fast and accurate algorithm for the influence maximization problem under the independent cascade model.
Given a graph with edge probabilities, it selects a seed set of a given size.
$ make
$ ./benchmark graph k R
- graph: input file (see below)
- k: the seed set size
- R: the number of random graphs (larger = more accurate)
$ make
$ ./benchmark ./sample_graph.tsv 5 200
0-th seed = 4
1-th seed = 0
2-th seed = 2
3-th seed = 3
4-th seed = 1
u_1 v_1 p_1
...
u_i v_i p_i
...
u_m v_m p_m
- The i-th line means an directed edge (u_i, v_i) with propagation probability p_i (see
sample_graph.tsv
). - Vertices should be described by integers starting from zero.
- Naoto Ohsaka, Takuya Akiba, Yuichi Yoshida, and Ken-ichi Kawarabayashi. Fast and Accurate Influence Maximization on Large Networks with Pruned Monte-Carlo Simulations. In AAAI-14, pages 138--144, 2014.