  Graph embedding provides an efficient solution for graph analysis by
converting the graph into a low-dimensional space which preserves the structure
information. In contrast to the graph structure data, the i.i.d. node embedding
can be processed efficiently in terms of both time and space. Current
semi-supervised graph embedding algorithms assume the labelled nodes are given,
which may not be always true in the real world. While manually label all
training data is inapplicable, how to select the subset of training data to
label so as to maximize the graph analysis task performance is of great
importance. This motivates our proposed active graph embedding (AGE) framework,
in which we design a general active learning query strategy for any
semi-supervised graph embedding algorithm. AGE selects the most informative
nodes as the training labelled nodes based on the graphical information (i.e.,
node centrality) as well as the learnt node embedding (i.e., node
classification uncertainty and node embedding representativeness). Different
query criteria are combined with the time-sensitive parameters which shift the
focus from graph based query criteria to embedding based criteria as the
learning progresses. Experiments have been conducted on three public data sets
and the results verified the effectiveness of each component of our query
strategy and the power of combining them using time-sensitive parameters. Our
code is available online at: https://github.com/vwz/AGE.
