  Expert finding, a popular service provided by many online websites such as
Expertise Finder, LinkedIn, and AMiner, benefits seeking consultants,
collaborators, and candidate qualifications. However, its quality is suffered
from a single source of support information for experts. This paper employs
AMiner, a free online academic search and mining system, having collected more
than over 100 million researcher profiles together with 200 million papers from
multiple publication databases, as the basis for investigating the problem of
expert linking, which aims at linking any external information of persons to
experts in AMiner. A critical challenge is how to perform zero shot expert
linking without any labeled linkages from the external information to AMiner
experts, as it is infeasible to acquire sufficient labels for arbitrary
external sources. Inspired by the success of self supervised learning in
computer vision and natural language processing, we propose to train a self
supervised expert linking model, which is first pretrained by contrastive
learning on AMiner data to capture the common representation and matching
patterns of experts across AMiner and external sources, and is then fine-tuned
by adversarial learning on AMiner and the unlabeled external sources to improve
the model transferability. Experimental results demonstrate that COAD
significantly outperforms various baselines without contrastive learning of
experts on two widely studied downstream tasks: author identification
(improving up to 32.1% in HitRatio@1) and paper clustering (improving up to
14.8% in Pairwise-F1). Expert linking on two genres of external sources also
indicates the superiority of the proposed adversarial fine-tuning method
compared with other domain adaptation ways (improving up to 2.3% in
HitRatio@1).
