In spite of the excellent performance on many real-world networks, this family of approaches usually has 'resolution limit' problems, i.e., modularity-based methods favor larger communities and fail to discover communities of small sizes [25],[25].
Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues the quantitative function for community as well as algorithms to discover communities.
In the west, social workers tend to work with individuals and families, and are now increasingly re-discovering community work.
High values of Q imply high values of l s for each discovered community.
We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.
This suggests a framework to structure future research in discovering community smells, but also suggests an intrinsic difficulty to measure social debt in a precise way, depending on non-trivial relations and characteristics with implicit relations.
We explore the application of an inexpensive and less-invasive method of discovering community patterning at a series of complex sites in the glacio-lacustrine region of northern Ohio.
In particular, social networks are being represented in the form of graphs to address fundamental problems, such as discovering communities in the network [4, 5] or discovering the community that is most likely to contain the query node.
