Effective and Efficient Spectral Clustering on Text and Link Data

Clustering text and link data, as an important task in text and link analysis, aims at finding communities of linked documents by leveraging the information from both domains. Due to its improved performance over the single domain counterpart, it has attracted increasing attention from practitioners in recent years. Despite its popularity, all existing algorithms on clustering text and link data overlook the existence of domain-specific distinctions and thus result in unsatisfactory clustering quality. In this paper, we address this limitation by explicitly modeling the domain-specific distinctions in the clustering process. Specifically, we extend the idea of consensus and domain-specific subspace decomposition from flat data to graph data. Such a modeling, when coupled with a regularization to further sharpen the information distinction, makes the consensus information between text and link more accurate for clustering with both domains. The final model is cast into the spectral clustering model by imposing the subspace orthogonality. To eschew the costly eigen-decomposition required for spectral clustering and further speed-up the optimization, we take advantage of the data sparsity and the low dimensionality of subspaces, and deploy a constraint-preserving gradient method to efficiently solve the model. The experimental study on three real datasets shows that our algorithm consistently and significantly outperforms the state-of-the-art relevant algorithms in terms of both quality and efficiency.