Abstract
Graph pattern mining (GPM) is a key building block in diverse applications, including bioinformatics, chemical engineering, social network analysis, recommender systems and security. Existing GPM frameworks either provide high-level interfaces for productivity at the cost of expressiveness or provide low-level interfaces that can express a wide variety of GPM algorithms at the cost of increased programming complexity. Moreover, existing systems lack the flexibility to explore combinations of optimizations to achieve performance competitive with hand-optimized applications.
Highlights
Graph pattern mining (GPM) problems exist in many application domains [5, 16, 18, 22]
We show that existing optimizations in the literature applied to specific problems/applications can be applied more generally to other GPM problems
If v1 and v2 occur in a vertex-induced subgraph, e occurs in the subgraph as well; in an edge-induced subgraph, edge e will be present only if it is in the given edge set F
Summary
Graph pattern mining (GPM) problems exist in many application domains [5, 16, 18, 22]. One example is motif counting [7, 25, 43], which counts the number of occurrences of certain structural patterns in a given graph (Fig. 1). Given a vertex set W ⊆ V , the vertex-induced subgraph is the graph G whose (1) vertex set is W and whose (2) edge set contains the edges in E whose endpoints are in W. Given an edge set F ⊆ E, the edge-induced subgraph is the graph G whose (1) edge set is F and whose (2) vertex set contains the endpoints in V of the edges in F. When f is a mapping of a graph onto itself, i.e., G1 and G2 are the same graph, G1 and G2 are automorphic, i.e. G1 G2
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