Frequent graph mining is an important though computationally hard problem because it requires enumerating possibly an exponential number of candidate subgraph patterns, and checking their presence in a database of graphs. In this paper, we propose a novel approach for parallel graph mining on GPUs, which have emerged as a relatively cheap but powerful architecture for general purpose computing....

Graph mining approaches are extremely popular and effective in molecular databases. The vast majority of these approaches first derive interesting, i.e. frequent, patterns and then use these as features to build predictive models. Rather than building these models in a two step indirect way, the SMIREP system introduced in this paper, derives predictive rule models from molecular data directly....

In this study, we formulate the concept of “mining maximal-size frequent subgraphs” in the challenging domain of visual data (images and videos). In general, visual knowledge can usually be modeled as attributed relational graphs (ARGs) with local attributes representing local parts and pairwise attributes describing the spatial relationship between parts. Thus, from a practical perspective, su...

In the early years of data mining and knowledge discovery in databases, method development focused on rigidly and plainly structured data. Most often efforts were even confined to data that can be represented as a simple table, which describes a set of sample cases by attribute-value pairs. Recent years, however, have seen a constantly growing interest in the analysis of more complex data, with...

Large real-world graphs often show interesting properties, such as power-law degree distributions and very small diameters. Discovering such patterns and regularities has a wide range of potential applications. It could help us with detecting outliers or abnormal subnetworks (such as terrorist networks or illegal money-laundering rings), maximizing e ciency of disease controlling, marketing, fo...

In this paper we introduce graph-evolution rules, a novel type of frequency-based pattern that describe the evolution of large networks over time, at a local level. Given a sequence of snapshots of an evolving graph, we aim at discovering rules describing the local changes occurring in it. Adopting a definition of support based on minimum image we study the problem of extracting patterns whose ...

What are the primary “characteristics” of large real-world graphs? How can we spot “outlier” nodes and edges in such graphs? Can we generate synthetic but “realistic” graphs? How quickly will a virus spread over such a graph? Will the virus die out or will it become an epidemic? What are the primary “groups” of nodes in such a graph? These questions show up in one form or the other in many appl...