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Journal of the ACM (JACM)
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On power-law relationships of the Internet topology
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Algorithm 447: efficient algorithms for graph manipulation
Communications of the ACM
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ACM Computing Surveys (CSUR)
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IEEE/ACM Transactions on Networking (TON)
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SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
The webgraph framework I: compression techniques
Proceedings of the 13th international conference on World Wide Web
Graph distances in the streaming model: the value of space
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
On graph problems in a semi-streaming model
Theoretical Computer Science - Automata, languages and programming: Algorithms and complexity (ICALP-A 2004)
Data streams: algorithms and applications
Foundations and Trends® in Theoretical Computer Science
Graph Spanners in the Streaming Model: An Experimental Study
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ACM Transactions on Algorithms (TALG)
Finding graph matchings in data streams
APPROX'05/RANDOM'05 Proceedings of the 8th international workshop on Approximation, Randomization and Combinatorial Optimization Problems, and Proceedings of the 9th international conference on Randamization and Computation: algorithms and techniques
Computing strongly connected components in the streaming model
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
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In this article, we present the first algorithm in the streaming model to characterize completely the biconnectivity properties of undirected networks: articulation points, bridges, and connected and biconnected components. The motivation of our work was the development of a real-time algorithm to monitor the connectivity of the autonomous systems (AS) network, but the solution provided is general enough to be applied to any network. The network structure is represented by a graph, and the algorithm is analyzed in the datastream framework. Here, as in the on-line model, the input graph is revealed one item (i.e., graph edge) after the other, in an on-line fashion; but, if compared to traditional on-line computation, there are stricter requirements for both memory occupation and per item processing time. Our algorithm works by properly updating a forest over the graph nodes. All the graph (bi)connectivity properties are stored in this forest. We prove the correctness of the algorithm, together with its space (O(nlog n), with n being the number of nodes in the graph) and time bounds. We also present the results of a brief experimental evaluation against real-world graphs, including many samples of the AS network, ranging from medium to massive size. These preliminary experimental results confirm the effectiveness of our approach. © 2012 Wiley Periodicals, Inc. NETWORKS, Vol. 2012 © 2012 Wiley Periodicals, Inc.