Learning Network Representations With Different Order Structural Information

Network embeddings aim to learn representations of nodes in a network with both the first- and the high-order proximities preserved. The first-order proximity corresponds to network reconstruction, while the high-order proximity is in tune with network inference. Since the tradeoff between the two proximities varies on scenarios, we propose an adjustable network embedding (ANE) algorithm for adjusting the weight between the first- and the high-order proximities. ANE is based on two hypotheses: 1) nodes in closed triplets are more important than nodes in open triplets and 2) closed triplets with higher degrees are more important. In addition, we change the bidirectional sampling of Word2vec into directional sampling to preserve the frequency of node pairs in the training set. Three common tasks, network reconstruction, link prediction, and classification are conducted on various publicly available data sets to validate the abovementioned statements.