In data models that have graph representations, users navigate following the links of the graph structure. Conducting data mining on collected information about user accesses in such models, involves the determination of frequently occurring access sequences. In this paper, we examine the problem of nding traversal patterns from such collections. The determination of patterns is based on the gr...

T he ever-evolving universe of social networks offers new tools to improve users’ experience and facilitate their communication. This evolution presents opportunities and challenges for data miners, with further pressure to provide approaches that are suitable at the big data scale and in highly dynamic contexts. Moreover, besides pure graph mining, other attributes on vertices and edges (query...

How does the Web look? How could we tell an “abnormal” social network from a “normal” one? These and similar questions are important in many fields where the data can intuitively be cast as a graph; examples range from computer networks, to sociology, to biology, and many more. Indeed, any M : N relation in database terminology can be represented as a graph. Many of these questions boil down to...

The incredible rising of on-line social networks gives a new and very strong interest to the set of techniques developed since several decades to mining graphs and social networks. In particularly community detection methods can bring very valuable informations about the structure of an existing social network in the Business Intelligence framework. In this chapter we give a large view, firstly...

Graphs show up in a surprisingly diverse set of disciplines, ranging from computer networks to sociology, biology, ecology and many more. How do such “normal” graphs look like? How can we spot abnormal subgraphs within them? Which nodes/edges are “suspicious?” How does a virus spread over a graph? Answering these questions is vital for outlier detection (such as terrorist cells, money launderin...

Mining of transliterations from comparable or parallel text can enhance natural language processing applications such as machine translation and cross language information retrieval. This paper presents an enhanced transliteration mining technique that uses a generative graph reinforcement model to infer mappings between source and target character sequences. An initial set of mappings are lear...

Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired...