Fast Streaming Small Graph Canonization
In this paper, we introduce the streaming graph canonization problem. Its goal is finding a canonical representation of a sequence of graphs in a stream. Our model of a stream fixes the graph’s vertices and allows for fully dynamic edge changes, meaning it permits both addition and removal of edges. Our focus is on small graphs, since small graph isomorphism is an important primitive of many subgraph-based metrics, like motif analysis or frequent subgraph mining. We present an efficient data structure to approach this problem, namely a graph isomorphism discrete finite automaton and showcase its efficiency when compared to a non-streaming-aware method that simply recomputes the isomorphism information from scratch in each iteration.
This work is partly financed by ERDF within project “POCI-01-0145-FEDER-006961”, by FCT as part of project “UID/EEA/50014/2013”, and by FourEyes, a research line within “TEC4Growth/NORTE-01-0145-FEDER-000020” financed by NORTE2020 through ERDF.
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