Introducing New Node Prediction in Graph Mining: Predicting All Links
  from Isolated Nodes with Graph Neural Networks
Damiano Zanardini Emilio Serrano
This paper introduces a new problem in the field of graph mining and social
network analysis called new node prediction. More technically, the task can be
categorized as zero-shot out-of-graph all-links prediction. This challenging
problem aims to predict all links from a new, isolated, and unobserved node
that was previously disconnected from the graph. Unlike classic approaches to
link prediction (including few-shot out-of-graph link prediction), this problem
presents two key differences: (1) the new node has no existing links from which
to extract patterns for new predictions; and (2) the goal is to predict not
just one, but all the links of this new node, or at least a significant part of
them. Experiments demonstrate that an architecture based on Deep Graph Neural
Networks can learn to solve this challenging problem in a bibliographic
citation network.