Finding all the negative cycles in a directed graph
Discrete Applied Mathematics
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Graph mining: Laws, generators, and algorithms
ACM Computing Surveys (CSUR)
Collusion set detection using graph clustering
Data Mining and Knowledge Discovery
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Transaction data in domains such as trading records from online financial or other markets, logistics delivery registers, and many others, are being accumulated at an increasing rate. In this type of data each transaction has a complex format, being usually associated with attributes such as time, numerical quantity, parties involved, and so on. Performing data mining on trace record data of complex transactions may enable the extraction of knowledge about implicit relationships which will benefit the community in different ways, for example by improving market efficiency and oversight, or detecting scheduling bottlenecks. However, the size of data sets of this type is usually enormous, and therefore in order to perform searching or mining techniques considerations of efficiency are often more important than correctness. In this paper we develop a framework to embed different methods to speed up search algorithms with the goal of detecting cycles in trace record data that fit a given constraint predicate on the amount of transaction quantities that can flow in a direction. The method is shown to improve significantly on a naïve approach, and suggests a number of directions for further work.