Multi-view networks are ubiquitous in real-world applications. In order to extract knowledge or business value, it is of interest to transform such networks into representations that are easily machineactionable. Meanwhile, network embedding has emerged as an e ective approach to generate distributed network representations. erefore, we are motivated to study the problem of multi-view network embedding, with a focus on the characteristics that are speci c and important in embedding this type of networks. In our practice of embedding real-world multi-view networks, we identify two such characteristics, which we refer to as preservation and collaboration. We then explore the feasibility of achieving be er embedding quality by simultaneously modeling preservation and collaboration, and propose the 2 algorithms. With experiments on a series of synthetic datasets, an internal Snapchat dataset, and two public datasets, we further con rm the presence and importance of preservation and collaboration. ese experiments also demonstrate that be er embedding can be obtained by simultaneously modeling the two characteristics, while not over-complicating the model or requiring additional supervision.
Mvn2vec REF explores the feasibility to achieve better embedding results by simultaneously modeling preservation and collaboration to represent semantic meanings of edges in different views respectively.
mvn2vec: Preservation and Collaboration in Multi-View Network Embedding
ArXiv
ArXiv
Computer Science