In this paper, we propose a new hybrid graph-based weight framework which considers for both connection types and distributional information of the class in the dataset. This framework derives the weighting information from the connections, connection types, and distributional information of observed label classes. We will elaborate this in the methodology section in this paper. A weighted associative classifier (IGAC) will be developed and applied to three biomedical datasets.