As a particular type of social networking site, online academic communities have revolutionized the way researchers collaborate and communicate with each other. With the growth of the number of users registered on online academic communities, the information overload problem presents a great challenge for researchers to find relevant and reliable friends there. To differentiate from friends in conventional social networking sites, friends in online academic community are named as scholar-friends in this research. Scholar-friend recommendation in online academic communities involves different entities (e.g., researchers, research articles, affiliations, research interests and status updates) and various relationships among entities (e.g., scholar-friend relationship of researchers, post relationship between researchers and status updates, and writing relationship between researchers and papers), which constitute a complex heterogeneous network. By leveraging the entity and relationship data in online academic community, this research proposes a heterogeneous network based approach to recommend scholar-friends where information gain is used to identify valuable meta paths and a regularization based optimization is employed to make personalized recommendation for each individual researcher.