Current autograders of programming assignments are typically program output based; they fall short in many ways: e.g. they do not carry out subjective evaluations such as code quality, or whether the code has followed any instructor specified constraints; this is still done manually by teaching assistants. In this paper, we tackle a specific aspect of such evaluation: to verify whether a program implements a specific \emph{algorithm} that the instructor specified. An algorithm, {\em e.g.} bubble sort, can be coded in myriad different ways, but a human can always understand the code and spot, say a bubble sort, {\em vs.} a selection sort. We develop and compare four approaches to do precisely this: given the source code of a program known to implement a certain functionality, \emph{identify the algorithm} used, among a known set of algorithms. The approaches are based on code similarity, Support Vector Machine (SVM) with tree or graph kernels, and transformer neural architectures based only source code (CodeBERT), and the extension of this that includes code structure (GraphCodeBERT). We further use a model for explainability (LIME) to generate insights into why certain programs get certain labels. Results based on our datasets of sorting, searching and shortest path codes, show that GraphCodeBERT, fine-tuned with \emph{scrambled source code}, {\em i.e.}, where identifiers are replaced consistently with arbitrary words, gives the best performance in algorithm identification, with accuracy of 96-99% depending on the functionality, including correct classification of obfuscated source code.