2017/01/16 by Jiarong Xie, Jia-Rong Xie, Xie, Jia-Rong +2
Computer Science · Mathematics · Physics and Astronomy · #Artificial intelligence #Combinatorics #Computer science #Entropy (arrow of time) #FOS: Computer and information sciences #FOS: Physical sciences #Function (biology) #Inference #Mathematics #Metadata #Modularity (biology) #Neural Networks and Applications #Partition (number theory) #Physics and Society (physics.soc-ph) #Principle of maximum entropy #Social and Information Networks (cs.SI) #Statistical inference #Statistics #Theoretical computer science #cs.SI #physics.soc-ph
paper · pdf · doi:10.48550/arxiv.1701.04241
arxiv created 2017/01/16 · openalex publication_date 2017/01/16 · arxiv updated 2017/02/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.