Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active
  Learning
Tianmeng Yang Min Zhou Yujing Wang Zhengjie Lin Lujia Pan Bin Cui Yunhai Tong
Graph Active Learning (GAL), which aims to find the most informative nodes in
graphs for annotation to maximize the Graph Neural Networks (GNNs) performance,
has attracted many research efforts but remains non-trivial challenges. One
major challenge is that existing GAL strategies may introduce semantic
confusion to the selected training set, particularly when graphs are noisy.
Specifically, most existing methods assume all aggregating features to be
helpful, ignoring the semantically negative effect between inter-class edges
under the message-passing mechanism. In this work, we present Semantic-aware
Active learning framework for Graphs (SAG) to mitigate the semantic confusion
problem. Pairwise similarities and dissimilarities of nodes with semantic
features are introduced to jointly evaluate the node influence. A new
prototype-based criterion and query policy are also designed to maintain
diversity and class balance of the selected nodes, respectively. Extensive
experiments on the public benchmark graphs and a real-world financial dataset
demonstrate that SAG significantly improves node classification performances
and consistently outperforms previous methods. Moreover, comprehensive analysis
and ablation study also verify the effectiveness of the proposed framework.