The class of frequent hypergraph mining problems is introduced which includes the frequent graph mining problem class and contains also the frequent itemset mining problem. We study the computational properties of different problems belonging to this class. In particular, besides negative results, we present practically relevant problems that can be solved in incremental-polynomial time. Some o...

Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSIGRAM (Spark based Single Graph Mining), a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, ...

Mining graph data is the extraction of novel and useful knowledge from a graph representation of data. The most natural form of knowledge that can be extracted from graphs is also a graph, we referred it as patterns. Many graph mining algorithms have been proposed in recent past researchers; all this algorithms rely on a very different approach so it’s really hard to say that which one is the m...

We present a tool for interactive exploration of graphs that integrates advanced graph mining methods in an interactive visualization framework. The tool enables efficient exploration and analysis of complex graph structures. For flexible integration of state-of-the-art graph mining methods, the viewer makes use of the open source data mining platform KNIME. In contrast to existing graph visual...

In recent years, graph mining has attracted much attention in the data mining community. Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summar...

Data mining has grown to include many more data types than the “traditional” flat files with numeric or categorical attributes. Images, text, video, and the internet are now areas of burgeoning data mining research. Graphical data is also an area of interest, since data in many domains—such as engineering design, network intrusion detection, fraud detection, criminology, document analysis, phar...

This paper proposes a general system for computationintensive graph mining tasks that find from a big graph all subgraphs that satisfy certain requirements (e.g., graph matching and community detection). Due to the broad range of applications of such tasks, many single-threaded algorithms have been proposed. However, graphs such as online social networks and knowledge graphs often have billions...

Frequent sub-graph mining entails two significant overheads. The first is concerned with candidate set generation. The second with isomorphism checking. These are also issues with respect to other forms of frequent pattern mining but are exacerbated in the context of frequent sub-graph mining. To reduced the search space, and address these twin overheads, a weighted approach to sub-graph mining...

Dense subgraph discovery is a key primitive in many graph mining applications, such as detecting communities in social networks and mining gene correlation from biological data. Most studies on dense subgraph mining only deal with one graph. However, in many applications, we have more than one graph describing relations among a same group of entities. In this paper, given two graphs sharing the...

In recent years, graph representations have been used extensively for modelling complicated structural information, such as circuits, images, molecular structures, biological networks, weblogs, XML documents and so on. As a result, frequent subgraph mining has become an important subfield of graph mining. This paper presents a novel Frequent Pattern Graph Mining algorithm, FP-GraphMiner, that c...