K-means algorithm happen to be one of the most common clustering algorithm which has been used over time. There are other clustering algorithms. Current clustering algorithms include hierarchical, k-medoid, AutoSOME, and k-means.

You can check the link here for clusterMaker which is a Cytoscape plugin for creating and visualizing clusters.

http://www.cgl.ucsf.edu/cytoscape/cluster/clusterMaker.shtml

You can try K-Means clustering. It is available in R package. For more detail you can go through the following link

https://www.r-bloggers.com/k-means-clustering-in-r/

You just need to mention the number of clusters.

For better results you can try spectral clustering. For the same You can explore following article

http://snap.stanford.edu/class/cs224w-2014/projects2014/cs224w-21-final.pdf

If you do not want to set any parameters (e.g. the number of clusters) manually, and would instead prefer automatic determination of these within your data, I recommend looking at Affinity Propagation clustering in either Python[1] or R[2].

A nice additional feature of this algorithm is that it also provides an exemplary member to each cluster.

[1] http://scikit-learn.org/stable/modules/generated/sklearn.cluster.AffinityPropagation.html

[2] https://cran.r-project.org/web/packages/apcluster/vignettes/apcluster.pdf