A network embedding is a representation of a large graph in a lowdimensional space, where vertices are modeled as vectors. The objective of a good embedding is to preserve the proximity (i.e., similarity) between vertices in the original graph. This way, typical search and mining methods (e.g., similarity search, kNN retrieval, classification, clustering) can be applied in the embedded space with the help of off-the-shelf multidimensional indexing approaches. Existing network embedding techniques focus on homogeneous networks, where all vertices are considered to belong to a single class. Therefore, they are weak in supporting similarity measures for heterogeneous networks. In this paper, we present an effective heterogeneous network embedding approach for meta path based proximity measures. We define an objective function, which aims at minimizing the distance between two distributions, one modeling the meta path based proximities, the other modeling the proximities in the embedded vector space. We also investigate the use of negative sampling to accelerate the optimization process. As shown in our extensive experimental evaluation, our method creates embeddings of high quality and has superior performance in several data mining tasks compared to state-of-the-art network embedding methods.
Huang and Mamoulis REF proposed a meta-path based proximity metric method.
Heterogeneous Information Network Embedding for Meta Path based Proximity
ArXiv
ArXiv
Computer Science