Fast Inference of Removal-Based Node Influence
WWW 2024(2024)
摘要
Graph neural networks (GNNs) are widely utilized to capture the information
spreading patterns in graphs. While remarkable performance has been achieved,
there is a new trending topic of evaluating node influence. We propose a new
method of evaluating node influence, which measures the prediction change of a
trained GNN model caused by removing a node. A real-world application is, "In
the task of predicting Twitter accounts' polarity, had a particular account
been removed, how would others' polarity change?". We use the GNN as a
surrogate model whose prediction could simulate the change of nodes or edges
caused by node removal. To obtain the influence for every node, a
straightforward way is to alternately remove every node and apply the trained
GNN on the modified graph. It is reliable but time-consuming, so we need an
efficient method. The related lines of work, such as graph adversarial attack
and counterfactual explanation, cannot directly satisfy our needs, since they
do not focus on the global influence score for every node. We propose an
efficient and intuitive method, NOde-Removal-based fAst GNN inference (NORA),
which uses the gradient to approximate the node-removal influence. It only
costs one forward propagation and one backpropagation to approximate the
influence score for all nodes. Extensive experiments on six datasets and six
GNN models verify the effectiveness of NORA. Our code is available at
https://github.com/weikai-li/NORA.git.
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