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Visualizing life in a graph stream

: Abello, J.; DeSimone, D.; Hadlak, S.; Schulz, H.-J.; Sumida, M.

Dehmer, M.:
Big data of complex networks
Boca Raton, Fla.: CRC Press, 2016
ISBN: 978-1-315-35359-3
ISBN: 978-1-4987-2361-9
ISBN: 978-1-4987-2362-6
ISBN: 978-1-315-37073-6
Aufsatz in Buch
Fraunhofer IGD, Institutsteil Rostock ()

When exploring a data stream it is natural to ask how to relate current stream snapshots to past snapshots. Depending on the data semantics and the task at hand different interpretations are possible. For example, in the case of microblog data (like Twitter) making sense of conversations and discussions related to a particular topic may entice users to join the discussion. For data analysts, a usual task is to discern how tweets-information-patterns spread with the possible goal of intuitively explaining their findings. In monitoring traffic scenarios, teasing out those communication patterns that deviate from a considered normal behavior can be used as proxies for intrusion detection. In general social networks, identifying influential nodes in a “volatile” graph stream is of considerable interest. We report here a useful approach to identify trends and exceptional nodes in a graph stream. The fundamental idea is to view a graph stream as a collection of “elementary”