Neural Embedding Propagation on Heterogeneous Networks Classification is one of the most important problems in machine learning. To address label scarcity, semisupervised learning (SSL) has been intensively studied over the past two decades, which mainly leverages data affinity modeled by networks. Label propagation (LP), however, as the most popular SSL technique, mostly only works on homogeneous networks with singletyped simple interactions. In this work, we focus on the more general and powerful heterogeneous networks, which accommodate multityped objects and links, and thus endure multityped complex interactions. Specifically, we propose neural embedding propagation (NEP), which leverages distributed embeddings to represent objects and dynamically composed modular networks to model their complex interactions. While generalizing LP as a simple instance, NEP is far more powerful in its natural awareness of different types of objects and links, and the ability to automatically capture their important interaction patterns. Further, we develop a series of efficient training strategies for NEP, leading to its easy deployment on realworld heterogeneous networks with millions of objects. With extensive experiments on three datasets, we comprehensively demonstrate the effectiveness, efficiency, and robustness of NEP compared with stateoftheart network embedding and SSL algorithms.

Inductive Embedding Learning on Attributed Heterogeneous Networks via Multitask SequencetoSequence Learning In the paper, we study the problem of inductive embedding learning on attributed heterogeneous networks, and propose a Multitask sequencetosequence learning based Inductive Network Embedding framework (MINE) capturing the attribute similarity, network proximity, and partial label information simultaneously. In particular, MINE trains an encoder function that aggregates information from a nodeu0027s longrange scope of contexts, with the node attribute sequences generated by the proposed typeguided heterogeneous random walk as inputs. We present an onetomany multitask sequencetosequence model where the encoder is shared between two related tasks: an unsupervised node identity sequence generation task to learn contextaware embeddings, and a semisupervised label prediction task to learn semanticsrich embeddings. Extensive experiments on realworld datasets demonstrate that the proposed method significantly outperforms several stateoftheart methods.