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Algorithms such as Affinity propagation make use of similarity matrix for performing Clustering, Also sparse matrices are preferred esp for large datasets. How does this differs from problem of Community Detection in networks. Since the Similarity Matrix can be considered as equivalent of Adjacency Matrix. (In networks where all nodes in network are of same type.) |
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Community detection is just a clustering problem with a similarity matrix, yes. The literature in the area has no problem stating this. It's just that it is a clustering problem with very specific difficulties (and also of interest to a slightly different community), so it has spawned a lot of specific research. Still, most of the methods they use are similar to traditional clustering methods.
This answer is marked "community wiki".
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reverted since the answer is good.
In addition to what Alexandre Passos said below, remember that the similarity matrix is often just the binary adjacency matrix. Often an explicit point is made of avoiding using any other information. This makes random-walk based methods a huge focus of community detection, whereas I don't think they're all that big in clustering.