Topic-sensitive expert finding based solely on heterogeneous academic networks

Identifying experts in a specific research field is an essential and practical task in academia and industry. Although some efforts have been attempted for expert finding, they rely on abundant text mining to reveal the target research topic accurately. Furthermore, it is time-consuming to collect and process these text materials for each candidate, and it is difficult to prepare a precise description for a topic that can cover all sub-topics. To this end, in this study, we develop a new expert finding model called TOpic-Sensitive representative Author identification (TOSA), to find representative authors in a specific research topic from a heterogeneous topic-insensitive academic network involving various topics, with a few well-known experts collected previously who are professional in the target topic. Specifically, we first learn the high-order relationships about authors in the heterogeneous academic network, which contain enough topic-related information. Next, an embedding space is found, where the collected experts are close to each other; in other words, the area they gather indicates the features about the collected authoritative experts. Finally, a prototype is calculated based on the collected experts in the embedding space, and we can rank other unlabeled authors by measuring the closeness between them and the prototype. Along this line, in addition to the collected experts in advance, more authoritative experts in the target topic will be identified. Extensive experiments on DBLP and ACM datasets demonstrate that the proposed TOSA model can identify experts who are professional in the query topic directly from a heterogeneous topic-insensitive academic network without text data processing, and its performance is significantly superior to the baseline models.