The search for frequent subgraphs is becoming increasingly important in many application areas including Web mining and bioinformatics. Any use of graph structures in mining, however, should also take into account that it is essential to integrate background knowledge into the analysis, and that patterns must be studied at different levels of abstraction. To capture these needs, we propose to u...

Graph Mining Under Linguistic Constraints to Explore Large Texts In this paper, we propose an approach to explore large texts by highlighting coherent sub-parts. The exploration method relies on a graph representation of the text according to the Hoey linguistic model which allows the selection and the binding of sentences in the graph. Our contribution relates to using graph mining techniques ...

Graph analytics is the process of discovering patterns and insights from data that can be modeled as graphs. Algorithms for graph analytics fall into two broad categories : Mining and Management. Graph mining algorithms are often used in graph management and vice versa. In recent times, these algorithms have become an indispensable tool for analyzing networks in domains such as i) Computational...

The concept of support is central to data mining. While the definition of support in transaction databases is intuitive and simple, that is not the case in graph datasets and databases. Most mining algorithms require the support of a pattern to be no grater than that of its subpatterns, a property called anti-monotonicity or admissibility. This study examines the requirements for admissibility ...

Graph structures provide a general framework for modeling entities and their relationships, and they are routinely used to describe a wide variety of data such as the Internet, the web, social networks, metabolic networks, protein-interaction networks, food webs, citation networks, and many more. In recent years, there has been an increasing amount of literature on studying properties, models, ...

The mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. Furthermore, much attention has recently been paid to frequent pattern mining from graph sequences (dynamic graphs or evolving graphs). In this paper, we define a novel class of subgraph subsequence called an “induced subgraph subsequence” to enable efficient mining of a complete set of freq...

Scientograms are a kind of graph representations depicting the state of Science in a specific domain. The automatic comparison and analysis of a set of scientograms, to show for instance the evolution of a scientific domain of a given country, is an interesting but challenging task as the handled data is huge and complex. In this paper, we aim to show that graph mining tools are useful to deal ...

We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining), which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic ...

Summary mining aims to find interesting summaries for a data set and to use data mining techniques to improve the functionality of Online Analytical Processing (OLAP) systems. In this paper, we propose an interactive summary mining approach, called GenSpace summary mining, to find the interesting summaries based on user expectations. In the mining process, to record the user’s evolving knowledg...

With the successful development of efficient algorithms for Frequent Subgraph Mining (FSM), this paper extends the scope of subgraph mining by proposing Vertex Unique labelled Subgraph Mining (VULSM). VULSM has a focus on the local properties of a graph and does not require external parameters such as the support threshold used in frequent pattern mining. There are many applications where the m...