Using graph data models for business intelligence applications is a novel and promising approach. In contrast to traditional data warehouse models, graph models enable the mining of relationship patterns. In our prior work, we introduced an approach to graph-based data integration and analytics called BIIIG (Business Intelligence with Integrated Instance Graphs). In this work, we compare state-...

The amount of available data is increasing very fast. With this data, the desire for data mining is also growing. More and larger databases have to be searched to find interesting (and frequent) elements and connections between them. Most often the data of interest is very complex. It is common to model complex data with the help of graphs consisting of nodes and edges that are often labeled to...

The upgrade of frequent item set mining to a setup with multiple relations —frequent query mining— poses many efficiency problems. Taking Object Identity as starting point, we present several optimization techniques for frequent query mining algorithms. The resulting algorithm has a better performance than a previous ILP algorithm and competes with more specialized graph mining algorithms in pe...

In graph mining applications, there has been an increasingly strong urge for imposing user-specified constraints on the mining results. However, unlike most traditional itemset constraints, structural constraints, such as density and diameter of a graph, are very hard to be pushed deep into the mining process. In this paper, we give the first comprehensive study on the pruning properties of bot...

As we become a more ’connected’ society, a greater need exists to understand complex network structures. While many in the field of data mining analyze network data, most models of networks are straightforward focusing on many connections of a single type. In order to better understand relationships between different types of entities and extract meaningful structure from heterogeneous data, da...

Overview Terminology: • Link Analysis: analysis of graph structures. • Web Structure Mining: analysis of the web graph. • Social Network Analysis: analysis graphs representing relationships between humans (social networks).

Graph classification is an increasingly important step in numerous application domains, such as function prediction of molecules and proteins, computerised scene analysis, and anomaly detection in program flows. Among the various approaches proposed in the literature, graph classification based on frequent subgraphs is a popular branch: Graphs are represented as (usually binary) vectors, with c...