  Estimating the similarity between two legal case documents is an important
and challenging problem, having various downstream applications such as
prior-case retrieval and citation recommendation. There are two broad
approaches for the task -- citation network-based and text-based. Prior
citation network-based approaches consider citations only to prior-cases (also
called precedents) (PCNet). This approach misses important signals inherent in
Statutes (written laws of a jurisdiction). In this work, we propose Hier-SPCNet
that augments PCNet with a heterogeneous network of Statutes. We incorporate
domain knowledge for legal document similarity into Hier-SPCNet, thereby
obtaining state-of-the-art results for network-based legal document similarity.
Both textual and network similarity provide important signals for legal case
similarity; but till now, only trivial attempts have been made to unify the two
signals. In this work, we apply several methods for combining textual and
network information for estimating legal case similarity. We perform extensive
experiments over legal case documents from the Indian judiciary, where the gold
standard similarity between document-pairs is judged by law experts from two
reputed Law institutes in India. Our experiments establish that our proposed
network-based methods significantly improve the correlation with domain
experts' opinion when compared to the existing methods for network-based legal
document similarity. Our best-performing combination method (that combines
network-based and text-based similarity) improves the correlation with domain
experts' opinion by 11.8% over the best text-based method and 20.6\% over the
best network-based method. We also establish that our best-performing method
can be used to recommend / retrieve citable and similar cases for a source
(query) case, which are well appreciated by legal experts.
