Incremental Graph Computations: Doable and Undoable

The incremental problem for a class \( {\mathcal {Q}} \) of graph queries aims to compute, given a query \( Q \in {\mathcal {Q}} \) , graph G, answers Q(G) to Q in G and updates ΔG to G as input, changes ΔO to output Q(G) such that Q(G⊕ΔG) = Q(G)⊕ΔO. It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q, ΔG and ΔO, which reduces the computations on possibly big G to small ΔG and ΔO. No matter how desirable, however, our first results are negative: For common graph queries such as traversal, connectivity, keyword search, pattern matching, and maximum cardinality matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental graph computation: (a) localizable, if its cost is decided by small neighbors of nodes in ΔG instead of the entire G; and (b) bounded relative to a batch graph algorithm \( {\mathcal {T}} \) , if the cost is determined by the sizes of ΔG and changes to the affected area that is necessarily checked by any algorithms that incrementalize \( {\mathcal {T}} \) . We show that the incremental computations above are either localizable or relatively bounded by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize existing batch graph algorithms by minimizing unnecessary recomputation. Using real-life and synthetic data, we experimentally verify the effectiveness of our incremental algorithms.