“…The restrictions imposed by classical streaming proved to be too strict to allow efficient solution for basic graph problems such as connectivity and shortest paths [15], and Feigenbaum et al [12], exploiting the idea originally introduced by Muthukrishnan [20], proposed the semi‐streaming model, in which the working memory size is O ( n p o l y l o g ( n )), where n is the number of nodes of the streaming graph: as in the semi‐external memory model [1,26], the main memory allows one to store data related to the nodes but not to the edges; Muthukrishnan [20] defines this memory requirement as a “sweet spot” for graph problems, and in this model several results appeared recently, including: connected components, bipartiteness, bipartite matching, minimum spanning tree [12,11], triangle counting [3], matching [17], and t‐spanners [2,9,11]. In particular, in the work of Feigenbaum et al [12], the authors present also an algorithm to compute the articulation points of a graph, but, since it uses a disjoint set data structure for each node of the input graph (to store the node's neighbors), its memory space requirements are not apparently within the bounds of the semi‐streaming model.…”