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Abstract: In this paper 1 [1] The extended abstract of this paper appeared on PPAM 2009, LNCS 6067 (2010) 11–20. we study the problem of interval incidence coloring of bipartite graphs. We show the upper bound for interval incidence coloring number ( ) for bipartite graphs , and we prove that holds for regular bipartite graphs. We solve this problem for subcubic bipartite graphs, i.e. we fully characterize the subcubic graphs that admit 4, 5 or 6 coloring, and we construct a linear time exact algorithm for subcubic bipartite graphs. We also study the problem for bipartite graphs with and we show that 5-coloring is easy and 6-coloring is hard ( -complete). Moreover, we construct an time optimal algorithm for trees. [Copyright &y& Elsevier]

In this paper, we study the problem of designing in-place algorithms for finding the maximum clique in the intersection graphs of axis-parallel rectangles and disks in R 2 . First, we propose an O ( n 2 log n ) time in-place algorithm for finding the maximum clique of the intersection graph of a set of n axis-parallel rectangles of arbitrary sizes. For the intersection graph of fixed height rectangles, the time complexity can be slightly improved to O ( n log n + n K ) , where K is the size of the maximum clique. For disk graphs, we consider two variations of the maximum clique problem, namely geometric clique and graphical clique. The time complexity of our algorithm for finding the largest geometric clique is O ( m log n + n 2 ) where m is the number of edges in the disk graph, and it works for disks of arbitrary radii. For graphical clique, our proposed algorithm works for unit disks (i.e., of same radii) and the worst case time complexity is O ( n 2 + m ( n + K 3 ) ) ; m is the number of edges in the unit disk intersection graph and K is the size of the largest clique in that graph. It uses O ( n 3 ) time in-place computation of maximum matching in a bipartite graph, where the vertices are given in an array, and the existence of an edge between a pair of vertices can be checked by an oracle on demand (from problem specification) in O ( 1 ) time. This problem is of independent interest. All these algorithms need O ( 1 ) work space in addition to the input array. [ABSTRACT FROM AUTHOR]