A unit cube in $k$ dimensional space (or \emph{$k$-cube} in short) is defined as the Cartesian product $R_1\times R_2\times...\times R_k$ where $R_i$(for $1\leq i\leq k$) is a closed interval of the form $[a_i,a_i+1]$ on the real line. A $k$-cube representation of a graph $G$ is a mapping of the vertices of $G$ to $k$-cubes such that two vertices in $G$ are adjacent if and only if their corresponding $k$-cubes have a non-empty intersection. The \emph{cubicity} of $G$, denoted as $\cubi(G)$, is the minimum $k$ such that $G$ has a $k$-cube representation. Roberts \cite{Roberts} showed that for any graph $G$ on $n$ vertices, $\cubi(G)\leq 2n/3$. Many NP-complete graph problems have polynomial time deterministic algorithms or have good approximation ratios in graphs of low cubicity. In most of these algorithms, computing a low dimensional cube representation of the given graph is usually the first step. We present an efficient algorithm to compute the $k$-cube representation of $G$ with maximum degree $��$ in $O(��\ln b)$ dimensions where $b$ is the bandwidth of $G$. Bandwidth of $G$ is at most $n$ and can be much lower. The algorithm takes as input a bandwidth ordering of the vertices in $G$. Though computing the bandwidth ordering of vertices for a graph is NP-hard, there are heuristics that perform very well in practice. Even theoretically, there is an $O(\log^4 n)$ approximation algorithm for computing the bandwidth ordering of a graph using which our algorithm can produce a $k$-cube representation of any given graph in $k=O(��(\ln b + \ln\ln n))$ dimensions. Both the bounds on cubicity are shown to be tight upto a factor of $O(\log\log n)$.

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