  In this paper 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 \emph{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 ($O(n\,\log n)$, 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.
