Data structures and network algorithms
Data structures and network algorithms
A matroid approach to finding edge connectivity and packing arborescences
Selected papers of the 23rd annual ACM symposium on Theory of computing
Sparsification—a technique for speeding up dynamic graph algorithms
Journal of the ACM (JACM)
A minimum spanning tree algorithm with inverse-Ackermann type complexity
Journal of the ACM (JACM)
An optimal minimum spanning tree algorithm
Journal of the ACM (JACM)
Using expander graphs to find vertex connectivity
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Finding Minimum Spanning Trees in O(m alpha(m,n)) Time
Finding Minimum Spanning Trees in O(m alpha(m,n)) Time
Graph distances in the streaming model: the value of space
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
Intractability of min- and max-cut in streaming graphs
Information Processing Letters
| Hi-index | 0.89 |
We present semi-streaming algorithms for basic graph problems thathave optimal per-edge processing times and therefore surpass allprevious semi-streaming algorithms for these tasks. Thesemi-streaming model, which is appropriate when dealing withmassive graphs, forbids random access to the input and restrictsthe memory to O(n·polylogn) bits.Particularly, the formerly best per-edge processing times forfinding the connected components and a bipartition areO(α(n)), for determining k-vertex andk-edge connectivity O(k2n) andO(n·logn) respectively for any constant k and forcomputing a minimum spanning forest O(logn). All these timebounds we reduce to O(1). Every presented algorithm determines asolution asymptotically as fast as the best corresponding algorithmup to date in the classical RAM model, which therefore cannotconvert the advantage of unlimited memory and random access intosuperior computing times for these problems.