Graph Distances in the Data-Stream Model

Joan Feigenbaum, Sampath Kannan, Andrew McGregor, Siddharth Suri, Jian Zhang
2009 SIAM journal on computing (Print)  
We explore problems related to computing graph distances in the data-stream model. The goal is to design algorithms that can process the edges of a graph in an arbitrary order given only a limited amount of working memory. We are motivated by both the practical challenge of processing massive graphs such as the web graph and the desire for a better theoretical understanding of the datastream model. In particular, we are interested in the trade-offs between model parameters such as perdata-item
more » ... rocessing time, total space, and the number of passes that may be taken over the stream. These trade-offs are more apparent when considering graph problems than they were in previous streaming work that solved problems of a statistical nature. Our results include the following: (1) Spanner construction: There exists a single-pass,Õ(tn 1+1/t )-space,Õ(t 2 n 1/t )-time-per-edge algorithm that constructs a (2t + 1)-spanner. For t = Ω(log n/log log n), the algorithm satisfies the semistreaming space restriction of O(n polylog n) and has per-edge processing time O(polylog n). This resolves an open question from [J. Feigenbaum et al., Theoret. Comput. Sci., 348 (2005), pp. 207-216]. (2) Breadth-first-search (BFS) trees: For any even constant k, we show that any algorithm that computes the first k layers of a BFS tree from a prescribed node with probability at least 2/3 requires either greater than k/2 passes orΩ(n 1+1/k ) space. Since constructing BFS trees is an important subroutine in many traditional graph algorithms, this demonstrates the need for new algorithmic techniques when processing graphs in the data-stream model. (3) Graph-distance lower bounds: Any t-approximation of the distance between two nodes requires Ω(n 1+1/t ) space. We also prove lower bounds for determining the length of the shortest cycle and other graph properties. (4) Techniques for decreasing per-edge processing: We discuss two general techniques for speeding up the per-edge computation time of streaming algorithms while increasing the space by only a small factor.
doi:10.1137/070683155 fatcat:uz3437bdh5bkpidj4zzd6vy4r4