  Extreme multi-label text classification (XMTC) aims to tag a text instance
with the most relevant subset of labels from an extremely large label set. XMTC
has attracted much recent attention due to massive label sets yielded by modern
applications, such as news annotation and product recommendation. The main
challenges of XMTC are the data scalability and sparsity, thereby leading to
two issues: i) the intractability to scale to the extreme label setting, ii)
the presence of long-tailed label distribution, implying that a large fraction
of labels have few positive training instances. To overcome these problems, we
propose GNN-XML, a scalable graph neural network framework tailored for XMTC
problems. Specifically, we exploit label correlations via mining their
co-occurrence patterns and build a label graph based on the correlation matrix.
We then conduct the attributed graph clustering by performing graph convolution
with a low-pass graph filter to jointly model label dependencies and label
features, which induces semantic label clusters. We further propose a
bilateral-branch graph isomorphism network to decouple representation learning
and classifier learning for better modeling tail labels. Experimental results
on multiple benchmark datasets show that GNN-XML significantly outperforms
state-of-the-art methods while maintaining comparable prediction efficiency and
model size.
