TY - GEN
T1 - DiffuGreedy
T2 - 7th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2018
AU - Panagopoulos, George
AU - Malliaros, Fragkiskos D.
AU - Vazirgiannis, Michalis
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Finding a set of nodes that maximizes the spread in a network, known as the influence maximization problem, has been addressed from multiple angles throughout the literature. Traditional solutions focus on the algorithmic aspect of the problem and are based solely on static networks. However, with the emergence of several complementary data, such as the network’s temporal changes and the diffusion cascades taking place over it, novel methods have been proposed with promising results. Here, we introduce a simple yet effective algorithm that combines the algorithmic methodology with the diffusion cascades. We compare it with four different prevalent influence maximization approaches, on a large scale Chinese microblogging dataset. More specifically, for comparison, we employ methods that derive the seed set using the static network, the temporal network, the diffusion cascades, and their combination. A set of diffusion cascades from the latter part of the dataset is set aside for evaluation. Our method outperforms the rest in both quality of the seed set and computational efficiency.
AB - Finding a set of nodes that maximizes the spread in a network, known as the influence maximization problem, has been addressed from multiple angles throughout the literature. Traditional solutions focus on the algorithmic aspect of the problem and are based solely on static networks. However, with the emergence of several complementary data, such as the network’s temporal changes and the diffusion cascades taking place over it, novel methods have been proposed with promising results. Here, we introduce a simple yet effective algorithm that combines the algorithmic methodology with the diffusion cascades. We compare it with four different prevalent influence maximization approaches, on a large scale Chinese microblogging dataset. More specifically, for comparison, we employ methods that derive the seed set using the static network, the temporal network, the diffusion cascades, and their combination. A set of diffusion cascades from the latter part of the dataset is set aside for evaluation. Our method outperforms the rest in both quality of the seed set and computational efficiency.
KW - Influence maximization
KW - Information spreading
KW - Large-scale network analysis
U2 - 10.1007/978-3-030-05411-3_32
DO - 10.1007/978-3-030-05411-3_32
M3 - Conference contribution
AN - SCOPUS:85059079703
SN - 9783030054106
T3 - Studies in Computational Intelligence
SP - 392
EP - 404
BT - Complex Networks and Their Applications VII - Volume 1 Proceedings The 7th International Conference on Complex Networks and their Applications COMPLEX NETWORKS 2018
A2 - Lambiotte, Renaud
A2 - Rocha, Luis M.
A2 - Lió, Pietro
A2 - Cherifi, Hocine
A2 - Aiello, Luca Maria
A2 - Cherifi, Chantal
PB - Springer Verlag
Y2 - 11 December 2018 through 13 December 2018
ER -