EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning
Hiroya Kato Kento Hasegawa Seira Hidano Kazuhide Fukushima
Graph Contrastive Learning (GCL) is unsupervised graph representation
learning that can obtain useful representation of unknown nodes. The node
representation can be utilized as features of downstream tasks. However, GCL is
vulnerable to poisoning attacks as with existing learning models. A
state-of-the-art defense cannot sufficiently negate adverse effects by poisoned
graphs although such a defense introduces adversarial training in the GCL. To
achieve further improvement, pruning adversarial edges is important. To the
best of our knowledge, the feasibility remains unexplored in the GCL domain. In
this paper, we propose a simple defense for GCL, EdgePruner. We focus on the
fact that the state-of-the-art poisoning attack on GCL tends to mainly add
adversarial edges to create poisoned graphs, which means that pruning edges is
important to sanitize the graphs. Thus, EdgePruner prunes edges that contribute
to minimizing the contrastive loss based on the node representation obtained
after training on poisoned graphs by GCL. Furthermore, we focus on the fact
that nodes with distinct features are connected by adversarial edges in
poisoned graphs. Thus, we introduce feature similarity between neighboring
nodes to help more appropriately determine adversarial edges. This similarity
is helpful in further eliminating adverse effects from poisoned graphs on
various datasets. Finally, EdgePruner outputs a graph that yields the minimum
contrastive loss as the sanitized graph. Our results demonstrate that pruning
adversarial edges is feasible on six datasets. EdgePruner can improve the
accuracy of node classification under the attack by up to 5.55% compared with
that of the state-of-the-art defense. Moreover, we show that EdgePruner is
immune to an adaptive attack.