  Code clone detection is about finding out similar code fragments, which has
drawn much attention in software engineering since it is important for software
maintenance and evolution. Researchers have proposed many techniques and tools
for source code clone detection, but current detection methods concentrate on
analyzing or processing code samples individually without exploring the
underlying connections among code samples. In this paper, we propose Gitor to
capture the underlying connections among different code samples. Specifically,
given a source code database, we first tokenize all code samples to extract the
pre-defined individual information. After obtaining all samples individual
information, we leverage them to build a large global sample graph where each
node is a code sample or a type of individual information. Then we apply a node
embedding technique on the global sample graph to extract all the samples
vector representations. After collecting all code samples vectors, we can
simply compare the similarity between any two samples to detect possible clone
pairs. More importantly, since the obtained vector of a sample is from a global
sample graph, we can combine it with its own code features to improve the code
clone detection performance. To demonstrate the effectiveness of Gitor, we
evaluate it on a widely used dataset namely BigCloneBench. Our experimental
results show that Gitor has higher accuracy in terms of code clone detection
and excellent execution time for inputs of various sizes compared to existing
state-of-the-art tools. Moreover, we also evaluate the combination of Gitor
with other traditional vector-based clone detection methods, the results show
that the use of Gitor enables them detect more code clones with higher F1.
