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In particular, we put in relation the social and topical groups we find algorithmically with the communities detected from the graph structure and we speculate about the impact that different group types may have in the process of information diffusion, following the intuition that information cascades and group boundaries are strictly related concepts [7].
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The optimal set of pseudo preference pairs is first detected from initial list by a modified graph-based method.
Deletions are detected in the spacer graph after the omission of order inversions.
A similar context is considered by Leroy et al. [41], who leverage the group membership information from Flickr to build a probabilistic graph and detect the hidden social graph with a good accuracy.
If the data were reported as only a graph, attempts were made to extracted data from the graph.
Although the different graph-based assemblers aim at constructing the overlapping graph, they apply different heuristics to compute a layout from the graph; we also use the idea of the de Bruijn graph in the seeds graph.
The data for Bergenstal 2010[ 37] was estimated from the graph.
Inhibitory concentration (IC50) was determined from the graph of % RSA.
The user can zoom into the graph, layout the graph by using different styles, remove selected elements from the graph or retrieve detailed information on selected genes.
In order to reduce the size of the GO graph for a visualization of the graph we delete iteratively non-significant parental terms from the graph.
Then we remove 5% of the detected outlier edges from the test graph data, and apply these graph clustering algorithms again to the new graph and record their NMI values and computational time.
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