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原文摘要

Pairwise graph ``alignments'', usually defined as maximum common subgraphs, have been considered in many application domains, including the comparison of molecular structures. Most commonly, the problem is formulated as maximum common induced subgraph (MCIS) or maximum common edge subgraph (MCES) problems. Sometimes these formulations are restricted to connected common subgraphs. Common subgraph problems readily generalize to more than two input graphs. However, this concept is fundamentally distinct from the notion of multiple sequence alignments. Abstractly, alignments are defined as super-structures from which the individual input objects are recovered via structure-preserving projections. While the mathematical construction readily applies to common induced subgraphs, it does not fully encompass the common edge subgraph problem, which is the variant more commonly employed in cheminformatics applications. In order to consistently define multiple edge-based alignments of graphs we represent a graph $G$ as E-graph $(E,\mathscr{V})$, where $E$ is the edge set of $G$ and $\mathscr{V}$ is the set of sets of incident edges. This representation implicitly encodes the vertices in terms of edge data. Alignments of E-graphs are themselves E-graphs that contain the input (E-)graphs as subgraphs. Maximum edge subgraphs correspond to maximum induced sub-E-graphs, and they can thus be computed using a slightly modified version of the VF2 algorithm without the need to switch from graphs to line graphs. Furthermore, multiple alignments of (E-)graphs yield a natural definition of a consensus graph that is ``on average'' present in the input without having to be a subgraph of every (and in fact of any) input graph.