  GitHub has become an important platform for code sharing and scientific
exchange. With the massive number of repositories available, there is a
pressing need for topic-based search. Even though the topic label functionality
has been introduced, the majority of GitHub repositories do not have any
labels, impeding the utility of search and topic-based analysis. This work
targets the automatic repository classification problem as keyword-driven
hierarchical classification. Specifically, users only need to provide a label
hierarchy with keywords to supply as supervision. This setting is flexible,
adaptive to the users' needs, accounts for the different granularity of topic
labels and requires minimal human effort. We identify three key challenges of
this problem, namely (1) the presence of multi-modal signals; (2) supervision
scarcity and bias; (3) supervision format mismatch. In recognition of these
challenges, we propose the HiGitClass framework, comprising of three modules:
heterogeneous information network embedding; keyword enrichment; topic modeling
and pseudo document generation. Experimental results on two GitHub repository
collections confirm that HiGitClass is superior to existing weakly-supervised
and dataless hierarchical classification methods, especially in its ability to
integrate both structured and unstructured data for repository classification.
