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Transfer learning across graphs drawn from different distributions (domains) is in great demand across many applications, yet the empirical performances vary and the in-depth understanding has been lacking. In this talk, I will first introduce our recent efforts on using inductive graph neural networks (GNNs) to solve the general cold-start problem of isolated "tail" nodes, transferring knowledge from the "head" nodes in the same graph yet with much richer neighborhood information. I will thendoi:10.5281/zenodo.6501657 fatcat:heekjiz4djbrhhcc75xsdxhiie