A symbol decoding and graph generation algorithm for online handwritten mathematical expression recognition is formulated. It differs from our previous system and most other systems in two aspects: 1) it embeds stroke grouping into symbol identification to form a unified probabilistic framework for symbol recognition; and 2) a symbol graph rather than a list of symbol sequence hypotheses is generated, which makes post-processing with new information possible. Experimental results show that high quality symbol graph can be generated by the proposed algorithm. Symbol sequence corresponding to the best path in the graph demonstrates much higher symbol recognition accuracy than before, especially after rescoring with trigram. Math formula recognition performance is significantly improved.