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On Summarizing Graph Streams by Tang, Nan; Chen, Qing; Mitra, Prasenjit is a scholarly article available to read on EtoBox.

What is On Summarizing Graph Streams about?

Graph streams, which refer to the graph with edges being updated sequentially in a form of a stream, have wide applications such as cyber security, social networks and transportation networks. This paper studies the problem of summarizing graph streams. Specifically, given a graph stream G, directed or undirected, the objective is to summarize G as S with much smaller (sublinear) space, linear construction time and constant maintenance cost for each edge update, such that S allows many queries over G to be approximately conducted efficiently. Due to the sheer volume and highly dynamic nature of graph streams, summarizing them remains a notoriously hard, if not impossible, problem. The widely used practice of summarizing data streams is to treat each element independently by e.g., hash- or sampling-based method, without keeping track of the connections between elements in a data stream, which gives these summaries limited power in supporting complicated queries over graph streams. This paper discusses a fundamentally different philosophy for summarizing graph streams. We present gLava, a probabilistic graph model that, instead of treating an edge (a stream element) as the operating

Author

Tang, Nan; Chen, Qing; Mitra, Prasenjit

Published

2015

Language

EN