arXiv subjects

Zhenhai Chang

Publications and source records attributed to Zhenhai Chang.

4 recordsLinked to original sources

arXiv2026

Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$): A Generalized Structure Detection Method for Attributed Networks

Community detection in attributed networks is one of the core tasks of complex network analysis. The stochastic block model (SBM) and its variants are able to detect generalized network structures such as assortative, disassortative and mixed structures. However, existing generative methods either ignore node heterogeneity, or characterize node activity in the topological and attribute dimensions by one and the same factor, making it difficult to correctly model node activity when the two kinds of activity are decoupled. In this paper, we propose the Dual-Corrected Stochastic Block Model ($\text{DC}^2\text{SBM}$), which introduces independent correction factors---a topological degree factor and an attribute abundance factor---into the link-generation and attribute-generation channels, respectively, thereby effectively decoupling node activity from membership inference, and which further introduces a global scaling parameter to resolve the mismatch between the normalization constraints and the network scale. The model is inferred by the expectation--maximization (EM) algorithm, and the newly introduced factors cancel out completely in the posterior computation, so that no additional computational cost is incurred. Experiments on synthetic and real attributed networks show that DC^2SBM outperforms state-of-the-art methods in community detection accuracy, with an especially pronounced advantage when the topological signal is weak. The two correction factors can be recovered independently from their respective channels without cross-talk. Analytical as well as controlled experiments further demonstrate that modeling with a single shared factor fails when the two kinds of activity are decoupled---a regime that is prevalent in real networks---thereby validating the necessity of dual independent correction.

cs.SI↗

arXiv2021

A Generative Node-attribute Network Model for Detecting Generalized Structure

Exploring meaningful structural regularities embedded in networks is a key to understanding and analyzing the structure and function of a network. The node-attribute information can help improve such understanding and analysis. However, most of the existing methods focus on detecting traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones. In this paper, based on the connectivity behavior of nodes and homogeneity of attributes, we propose a principle model (named GNAN), which can generate both topology information and attribute information. The new model can detect not only community structure, but also a range of other types of structure in networks, such as bipartite structure, core-periphery structure, and their mixture structure, which are collectively referred to as generalized structure. The proposed model that combines topological information and node-attribute information can detect communities more accurately than the model that only uses topology information. The dependency between attributes and communities can be automatically learned by our model and thus we can ignore the attributes that do not contain useful information. The model parameters are inferred by using the expectation-maximization algorithm. And a case study is provided to show the ability of our model in the semantic interpretability of communities. Experiments on both synthetic and real-world networks show that the new model is competitive with other state-of-the-art models.

cs.SI↗

arXiv2019

A Generative Model for Exploring Structure Regularities in Attributed Networks

Many real-world networks known as attributed networks contain two types of information: topology information and node attributes. It is a challenging task on how to use these two types of information to explore structural regularities. In this paper, by characterizing potential relationship between link communities and node attributes, a principled statistical model named PSB_PG that generates link topology and node attributes is proposed. This model for generating links is based on the stochastic blockmodels following a Poisson distribution. Therefore, it is capable of detecting a wide range of network structures including community structures, bipartite structures and other mixture structures. The model for generating node attributes assumes that node attributes are high dimensional and sparse and also follow a Poisson distribution. This makes the model be uniform and the model parameters can be directly estimated by expectation-maximization (EM) algorithm. Experimental results on artificial networks and real networks containing various structures have shown that the proposed model PSB_PG is not only competitive with the state-of-the-art models, but also provides good semantic interpretation for each community via the learned relationship between the community and its related attributes.

physics.soc-ph↗

arXiv2018

On approximate equivalence of modularity, D and non-negative matrix factorization

Community structures detection is one of the fundamental problems in complex network analysis towards understanding the topology structures of the network and the functions of it. Nonnegative matrix factorization (NMF) is a widely used method for community detection, and modularity Q and modularity density D are criteria to evaluate the quality of community structures. In this paper, we establish the connections between Q, D and NMF for the first time. Q maximization can be approximately reformulated under the framework of NMF with Frobenius norm, especially when $n$ is large, and D maximization can also be reformulated under the framework of NMF. Q minimization can be reformulated under the framework of NMF with Kullback-Leibler divergence. We propose new methods for community structures detection based on the above findings, and the experimental results on synthetic networks demonstrate their effectiveness.

cs.SI↗