  Identification and categorization of social media posts generated during
disasters are crucial to reduce the sufferings of the affected people. However,
lack of labeled data is a significant bottleneck in learning an effective
categorization system for a disaster. This motivates us to study the problem as
unsupervised domain adaptation (UDA) between a previous disaster with labeled
data (source) and a current disaster (target). However, if the amount of
labeled data available is limited, it restricts the learning capabilities of
the model. To handle this challenge, we utilize limited labeled data along with
abundantly available unlabeled data, generated during a source disaster to
propose a novel two-part graph neural network. The first-part extracts
domain-agnostic global information by constructing a token level graph across
domains and the second-part preserves local instance-level semantics. In our
experiments, we show that the proposed method outperforms state-of-the-art
techniques by $2.74\%$ weighted F$_1$ score on average on two standard public
dataset in the area of disaster management. We also report experimental results
for granular actionable multi-label classification datasets in disaster domain
for the first time, on which we outperform BERT by $3.00\%$ on average w.r.t
weighted F$_1$. Additionally, we show that our approach can retain performance
when very limited labeled data is available.
