Efficient representation of source code is essential for various software engineering tasks such as code search and code clone detection. One such technique for representing source code involves extracting paths from the AST and using a learning model to capture program properties. Code2vec is a commonly used path-based approach that uses an attention-based neural network to learn code embeddings which can then be used for various software engineering tasks. However, this approach uses only ASTs and does not leverage other graph structures such as Control Flow Graphs (CFG) and Program Dependency Graphs (PDG). Similarly, most recent approaches for representing source code still use AST and do not leverage semantic graph structures. Even though there exists an integrated graph approach (Code Property Graph) for representing source code, it has only been explored in the domain of software security. Moreover, it does not leverage the paths from the individual graphs. In our work, we extend the path-based approach code2vec to include semantic graphs, CFG, and PDG, along with AST, which is still largely unexplored in the domain of software engineering. We evaluate our approach on the task of MethodNaming using a custom C dataset of 730K methods collected from 16 C projects from GitHub. In comparison to code2vec, our approach improves the F1 Score by 11% on the full dataset and up to 100% with individual projects. We show that semantic features from the CFG and PDG paths are indeed helpful. We envision that looking at a mocktail of source code representations for various software engineering tasks can lay the foundation for a new line of research and a re-haul of existing research.