A generative node-attribute network model for detecting generalized structure and semantics

Abstract A challenge of community detection in attributed networks is how we can design an effective and efficient clustering method that can not only discover a wide of structure types but also have good community semantic annotations. To this end, by sharing the latent position of nodes, a mathematically principled model (named GNAN) that fuses topological information and node-attribute information is developed. Using the expectation–maximization algorithm, the latent position of each node and the model parameters are learned. The new model detects communities more accurately than can be done with topology information alone. And a case study is provided to show the ability of our model in the semantic interpretability of communities. In detail, firstly, inspired by the idea of NMM (Newman’s Mixture Models), a group of parameters that characterize the link behaviors of nodes is introduced into the topological model. In the probabilistic sense, nodes with the same link pattern form a community. Therefore, the combined model can generate not only traditional communities, i.e., groupings of nodes with dense internal connections and sparse external ones, 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. Secondly, based on the homogeneity assumption, another group of parameters describing the distribution of attributes in a community is introduced into the attributed model. Under the control of these parameters, the united model can generate different attributes according to the probability, and automatically discover the critical attributes of the community. Finally, experiments on both synthetic and real-world networks with various network structures show that the new model can detect communities more accurately than the related state-of-the-art models.