BLAST creates local sequence alignments by first building a database of small k-letter sub-sequences called k-mers. Identical k-mers from different regions provide seeds for longer local alignments. This seed-and-extend heuristic makes BLAST extremely fast and has led to its almost exclusive use despite the existence of more accurate, but slower, algorithms. In this paper, we introduce BLANT, the Basic Local Alignment for Networks Tool. BLANT is the analog of BLAST, but for networks: given an input graph, it samples small, induced, k-node subgraphs called k-graphlets. Graphlets have been used to classify networks, quantify structure, align networks both locally and globally, identify topology-function relationships, and build taxonomic trees without the use of sequences. Given an input network, BLANT produces millions of graphlet samples in seconds-orders of magnitude faster than existing methods. BLANT offers sampled graphlets in various forms: distributions of graphlets or their orbits, graphlet degree or graphlet orbit degree vectors, the latter being compatible with ORCA, or an index to be used as the basis for seed-and-extend local alignments. We demonstrate BLANTs usefelness by using its indexing mode to find functional similarity between yeast and human PPI networks.