Documentation updated

README.md

@@ -76,7 +76,9 @@ the risk respectively. See the [paper](http://arxiv.org/abs/1202.0425) for the m
 If the node base of the specified files is different (for example you decided to take the ground-truth clustering as a subset of the top K largest clusters) then it can be synchronized using the `-s` option. I.e. the nodes not present in the ground-truth clusters (communities) will be removed (also as the empty resulting clusters). The exception is thrown if the synchronization is not possible (in case the node base was not just reduced, rather it was totally different).
 
 # Related Projects
+- [xmeasures](https://github.com/eXascaleInfolab/xmeasures)  - Extrinsic clustering measures evaluation for the multi-resolution clustering with overlaps (covers): F1_gwah and NMI_om.
 - [OvpNMI](https://github.com/eXascaleInfolab/OvpNMI) - Another method of the NMI evaluation for the overlapping clusters (communities) that is not compatible with the standard NMI value unlike GenConvNMI, but it is much faster and yields exact results unlike probabilistic results with some variance in GenConvNMI.
+- [resmerge](https://github.com/eXascaleInfolab/resmerge)  - Resolution levels clustering merger with filtering. Flattens hierarchy/list of multiple resolutions levels (clusterings) into the single flat clustering with clusters on various resolution levels synchronizing the node base.
 - [ExecTime](https://bitbucket.org/lumais/exectime/)  - A lightweight resource consumption profiler.
 - [PyCABeM](https://github.com/eXascaleInfolab/PyCABeM) - Python Benchmarking Framework for the Clustering Algorithms Evaluation. Uses extrinsic (NMIs) and intrinsic (Q) measures for the clusters quality evaluation considering overlaps (nodes membership by multiple clusters).