Abstract-Temporal information is increasingly available with network data sets. This information can expose underlying processes in the data via sequences of link activations. Examples range from the propagation of ideas through a scientific collaboration network, to the spread of disease via contacts between infected and susceptible individuals. We focus on the flow of funds through an online financial transaction network, in which given patterns might signify suspicious behaviour. The search for these patterns may be formulated as a temporally constrained subgraph isomorphism problem. We compare two algorithms which use temporal data at different stages during the search, and empirically demonstrate one to be significantly more efficient.
Temporal information is increasingly available as part of large network data sets. This information reveals sequences of link activations between network entities, which can expose underlying processes in the data. Examples include the dissemination of information through a social network, the propagation of musical ideas in a music sampling network, and the spread of a disease via contacts between infected and susceptible individuals. The search for these more meaningful patterns may be formulated as a time-respecting subgraph isomorphism problem. Our set of query graphs include an enumeration of small random graphs and fan-out-fan-in structures, all composed of time-respecting paths. We explore three methods of solving the problem, which differ in how they exploit temporal and topological information. One approach extracts all subgraphs that have the temporal properties we require and then performs subgraph isomorphism testing on each subgraph. Another approach performs subgraph isomorphism testing first with temporal post-filtering, while the other is a hybrid approach that uses temporal information during the search. We empirically demonstrate the hybrid approach to be more efficient than the others, over a range of network data sets. These data come from communication and social networks, up to 20 000 interactions in size.
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