With the proliferation of social networks, expert finding has gained significant research traction, as the experts(individuals) are used as hubs for knowledge sharing and dissemination, recommendation and endorsement, or even product promotion and marketing. Interestingly, expert finding becomes worthy when the discovered individuals are both knowledgeable and approachable. While knowledgeable expert finding is well studied, to the best of our knowledge this is the first ever work to study approachable exepert finding. In this research, we study the problem of finding approachable experts in a social network settings i.e., finding individuals who are knowledgeable on a given topic and are "most likely" to respond to the request. The aspect of approachability brings non-trivial challenges and opportunities in the modeling of the problem and solution.
We present our investigation using DBLP and Microsoft Academic Search datasets to demonstarte the effectiveness of our proposed solution on both small and large scale co-authorship network.