Active Sampling for Node Attribute Completion on Graphs

Node attribute is one kind of crucial information on graphs, but real-world graphs usually face attribute-missing problem where attributes of partial nodes are missing and attributes of the other nodes are available. It is meaningful to restore the missing attributes so as to benefit downstream graph learning tasks. Popular GNN is not designed for this node attribute completion issue and is not capable of solving it. Recent proposed Structure-attribute Transformer (SAT) framework decouples the input of graph structures and node attributes by a distribution matching technique, and can work on it properly. However, SAT leverages nodes with observed attributes in an equally-treated way and neglects the different contributions of different nodes in learning. In this paper, we propose a novel active sampling algorithm (ATS) to more efficiently utilize the nodes with observed attributes and better restore the missing node attributes. Specifically, ATS contains two metrics that measure the representativeness and uncertainty of each node's information by considering the graph structures, representation similarity and learning bias. Then, these two metrics are linearly combined by a Beta distribution controlled weighting scheme to finally determine which nodes are selected into the train set in the next optimization step. This ATS algorithm can be combined with SAT framework together, and is learned in an iterative manner. Through extensive experiments on 4 public benchmark datasets and two downstream tasks, we show the superiority of ATS in node attribute completion.