  As the social coding is becoming increasingly popular, understanding the
influence of developers can benefit various applications, such as advertisement
for new projects and innovations. However, most existing works have focused
only on ranking influential nodes in non-weighted and homogeneous networks,
which are not able to transfer proper importance scores to the real important
node. To rank developers in Github, we define developer's influence on the
capacity of attracting attention which can be measured by the number of
followers obtained in the future. We further defined a new method, DevRank,
which ranks the developers by influence propagation through heterogeneous
network constructed according to user behaviors, including "commit" and
"follow". Our experiment compares the performance between DevRank and some
other link analysis algorithms, the results have shown that DevRank can improve
the ranking accuracy.
