A Survey on Streaming Algorithms for Massive Graphs

Streaming is an important paradigm for handling massive graphs that are too large to fit in the main memory. In the streaming computational model, algorithms are restricted to use much less space than they would need to store the input. Furthermore, the input is accessed in a sequential fashion, therefore, can be viewed as a stream of data elements. The restriction limits the model and yet, algorithms exist for many graph problems in the streaming model. We survey a set of algorithms that compute graph statistics, matching and distance in a graph, and random walks. These are basic graph problems and the algorithms that compute them may be used as building blocks in graph-data management and mining.

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