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Network representations have been shown to improve performance within a variety of tasks, including classification, clustering, and link prediction. However, most models either focus on moderate-sized, homogeneous networks or require a significant amount of auxiliary input to be provided by the user. Moreover, few works have studied network representations in real-world heterogeneous social networks with ambiguous social connections and are often incomplete. In the present work, we investigate the problem of learning low-dimensional node representations in heterogeneous professional social networks (HPSNs), which are incomplete and have ambiguous social connections. We present a general heterogeneous network representation learning model called Star2Vec that learns entity and person embeddings jointly using a social connection strength-aware biased random walk combined with a node-structure expansion function. Experiments on LinkedIn's Economic Graph and publicly available snapshots of Facebook's network show that Star2Vec outperforms existing methods on members' industry and social circle classification, skill and title clustering, and member-entity link predictions. We also conducted large-scale case studies to demonstrate practical applications of the Star2Vec embeddings trained on LinkedIn's Economic Graph such as next career move, alternative career suggestions, and general entity similarity searches. 

With increased globalization and labor mobility, human resource reallocation across firms, industries and regions has become the new norm in labor markets. The emergence of massive digital traces of such mobility offers a unique opportunity to understand labor mobility at an unprecedented scale and granularity. While most studies on labor mobility have largely focused on characterizing macro-level (e.g., region or company) or micro-level (e.g., employee) patterns, the problem of how to accurately predict an employee's next career move (which company with what job title) receives little attention. This paper presents the first study of large-scale experiments for predicting next career moves. We focus on two sources of predictive signals: profile context matching and career path mining and propose a contextual LSTM model, NEMO, to simultaneously capture signals from both sources by jointly learning latent representations for different types of entities (e.g., employees, skills, companies) that appear in different sources. In particular, NEMO generates the contextual representation by aggregating all the profile information and explores the dependencies in the career paths through the Long Short-Term Memory (LSTM) networks. Extensive experiments on a large, real-world LinkedIn dataset show that NEMO significantly outperforms strong baselines and also reveal interesting insights in micro-level labor mobility.