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We develop a signal processing approach to the multiscale detection of communities in networks, that is of groups of nodes well connected together. The method relies on carefully engineered wavelets on graphs to introduce the notion of scale and to obtain a local view of the graph from each node. Computing the correlations between wavelets centered at different nodes, one has access to a notion of...
In an effort to simplify the analysis of data represented by networks, a classical approach is to uncover the community structure of the underlying graph. In this work, we take advantage of graph wavelets and the associated natural definition of scale to propose a multi-scale community mining tool. More precisely, at a given scale, we cluster nodes in the same community when their corresponding wavelets...
For data represented by networks, the community structure of the underlying graph is of great interest. A classical clustering problem is to uncover the overall “best” partition of nodes in communities. Here, a more elaborate description is proposed in which community structures are identified at different scales. To this end, we take advantage of the local and scale-dependent information encoded...
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