Modularity-like objective function in annotated networks

Citation

Xie, Jia-Rong & Wang, Bing-Hong (2017). Modularity-like objective function in annotated networks. arXiv.org.

Abstract

We ascertain the modularity-like objective function whose optimization is equivalent to the maximum likelihood in annotated networks. We demonstrate that the modularity-like objective function is a linear combination of modularity and conditional entropy. In contrast with statistical inference methods, in our method, the influence of the metadata is adjustable; when its influence is strong enough, the metadata can be recovered. Conversely, when it is weak, the detection may correspond to another partition. Between the two, there is a transition. This paper provides a concept for expanding the scope of modularity methods.

URL

https://doi.org/10.1007/s11467-017-0657-y

Reference Type

Journal Article

Journal Title

arXiv.org

Author(s)

Xie, Jia-Rong
Wang, Bing-Hong

Year Published

2017

DOI

10.1007/s11467-017-0657-y

Reference ID

9271