Cross-domain Network Representations
This addresses the limitation of existing methods that rely on domain-specific community structures, offering a solution for structural-scarce networks, though it appears incremental as it builds on random walk-based approaches.
The paper tackles the problem of learning network representations for domains lacking topological information by proposing CDNR, a cross-domain algorithm that transfers knowledge from structurally rich domains, achieving effective unsupervised representation for universal networks.
The purpose of network representation is to learn a set of latent features by obtaining community information from network structures to provide knowledge for machine learning tasks. Recent research has driven significant progress in network representation by employing random walks as the network sampling strategy. Nevertheless, existing approaches rely on domain-specifically rich community structures and fail in the network that lack topological information in its own domain. In this paper, we propose a novel algorithm for cross-domain network representation, named as CDNR. By generating the random walks from a structural rich domain and transferring the knowledge on the random walks across domains, it enables a network representation for the structural scarce domain as well. To be specific, CDNR is realized by a cross-domain two-layer node-scale balance algorithm and a cross-domain two-layer knowledge transfer algorithm in the framework of cross-domain two-layer random walk learning. Experiments on various real-world datasets demonstrate the effectiveness of CDNR for universal networks in an unsupervised way.