How can we find meaningful clusters in a graph robustly against noise edges?
Graph clustering (i.e., dividing nodes into groups of similar ones) is a
fundamental problem in graph analysis with applications in various fields.
Recent studies have demonstrated that graph neural network (GNN) based
approaches yield promising results for graph clustering. However, we observe
that their performance degenerates significantly on graphs with noise edges,
which are prevalent in practice. In this work, we propose MetaGC for robust
GNN-based graph clustering. MetaGC employs a decomposable clustering loss
function, which can be rephrased as a sum of losses over node pairs. We add a
learnable weight to each node pair, and MetaGC adaptively adjusts the weights
of node pairs using meta-weighting so that the weights of meaningful node pairs
increase and the weights of less-meaningful ones (e.g., noise edges) decrease.
We show empirically that MetaGC learns weights as intended and consequently
outperforms the state-of-the-art GNN-based competitors, even when they are
equipped with separate denoising schemes, on five real-world graphs under
varying levels of noise. Our code and datasets are available at
this https URL

我们提出了元学习图聚类方法 MetaGC，借助可分解的聚类损失函数以及元权重调整机制，能够对存在噪声边的图进行鲁棒的聚类，相较于现有的基于图神经网络的方法在各种噪声程度下的实验中，MetaGC 表现出更好的性能。