Active Sampling for Node Attribute Completion on Graphs
This work addresses a domain-specific problem for graph analysis by improving node attribute completion, which is incremental as it builds on the Structure-attribute Transformer framework.
The paper tackles the problem of missing node attributes in graphs by proposing an active sampling algorithm (ATS) that selects training nodes based on representativeness and uncertainty, achieving superior performance in node attribute completion on four benchmark datasets.
Node attribute, a type of crucial information for graph analysis, may be partially or completely missing for certain nodes in real world applications. Restoring the missing attributes is expected to benefit downstream graph learning. Few attempts have been made on node attribute completion, but a novel framework called Structure-attribute Transformer (SAT) was recently proposed by using a decoupled scheme to leverage structures and attributes. SAT ignores the differences in contributing to the learning schedule and finding a practical way to model the different importance of nodes with observed attributes is challenging. This paper proposes a novel AcTive Sampling algorithm (ATS) to restore missing node attributes. The representativeness and uncertainty of each node's information are first measured based on graph structure, representation similarity and learning bias. To select nodes as train samples in the next optimization step, a weighting scheme controlled by Beta distribution is then introduced to linearly combine the two properties. Extensive experiments on four public benchmark datasets and two downstream tasks have shown the superiority of ATS in node attribute completion.