Stock Crime Detection using Graph Mining

Outlier Detection using Graph Mining

Omni Graph Mining: Graph mining using RDBMS

In this study, we compare three approaches to implement graph algorithms SQL, MapReduce, and GraphChi in terms of performance and cost efficiency. Since MapReduce was introduced, it has been considered as a standard tool to analyze large scale data including graph data. However, we believe that there is a ‘sweet spot’ in which RDBMS can outperform MapReduce. We think that the performance of the...

Graph Mining and Communities Detection

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...

Metamorphic Malware Detection using Control Flow Graph Mining

Metamorphic malware propagation has persuaded the security society to consider about new approaches to confront this generation of malware with novel solutions. Control Flow Graph, CFG, has been successful in detection of simple malwares. By now, it needs to improve the CFG based detection methods to detect metamorphic malwares efficiently. Our Approach has improved the simple CFG with benefici...

Graph Mining using Graph Pattern Profiles

This paper presents our investigation into graph mining methods to help users understand large graphs. Our approach is a two-step process: First calculate subgraph labels and then calculate distribution statistics on these labels. Our approach is flexible in that it can identify a range of patterns from very abstract to very specific (e.g., isomorphisms). The statistics that we calculate can be...