Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
This work addresses the limitation of existing network embedding methods that fail to flexibly integrate auxiliary information like content and labels, offering a more comprehensive approach for network analysis applications.
The authors tackled the problem of network embedding by developing a unified matrix factorization framework that simultaneously incorporates network structure, node content, and label information, demonstrating improved performance on node classification and link prediction tasks across multiple real-world datasets.
Recent advances in the field of network embedding have shown the low-dimensional network representation is playing a critical role in network analysis. However, most of the existing principles of network embedding do not incorporate auxiliary information such as content and labels of nodes flexibly. In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously. For structure, we validate that the matrix we construct preserves high-order proximities of the network. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding in an unsupervised manner, i.e., without leveraging downstream classifiers. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the network in a matrix factorization framework. As a consequence, network embedding can be learned in a unified framework integrating network structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets.