How can we efficiently generate large-scale signed networks following real-world properties?
Due to its rich modeling capability of representing trust relations as positive and negative edges,
signed networks have spurred much interests with various applications.
Despite its importance, however, existing models for generating signed networks do not correctly reflect properties of real-world signed networks.
In this paper, we propose BalanSiNG
(Balanced Signed Network Generator), a novel, scalable, and fully parallelizable method for generating large-scale signed networks following realistic properties.
We identify a self-similar balanced structure observed from a real-world signed network, and simulate the self-similarity via Kronecker product.
Then, we exploit noise and careful weighting of signs
such that our resulting network obeys various properties of real-world signed networks.
BalanSiNG is easily parallelizable, and we implement it using Spark.
Extensive experiments show that BalanSiNG efficiently generates the most realistic signed networks satisfying various desired properties.
BalanSiNG is described in the following paper:
BalanSiNG: Fast and Scalable Generation of Realistic Signed Networks
Jinhong Jung, Ha-Myung Park, and U Kang
23rd International Conference on Extending Database Technology (EDBT) 2020, Copenhagen, Denmark.
The codes used in the paper are available.