Graph-Based Anomaly Detection (GBAD)

GBAD discovers anomalous instances of structural patterns in data, where the data represents entities, relationships and actions in graph form. Input to GBAD is a labeled graph in which entities are represented by labeled vertices and relationships or actions are represented by labeled edges between entities. Using the minimum description length (MDL) principle to identify the normative pattern that minimizes the number of bits needed to describe the input graph after being compressed by the pattern, GBAD embodies novel algorithms for identifying the three possible changes to a graph: modifications, insertions and deletions. Each algorithm discovers those substructures that match the closest to the normative pattern without matching exactly. As a result, GBAD is looking for those activities that appear to match normal (or legitimate) transactions, but in fact are structurally different.
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  Pattern Learning and Anomaly Detection in Streams (PLADS)

A generic algorithm for applying graph-based anomaly detection methods to streaming data. The user can apply any normative pattern discovery techniques and any graph-based anomaly detection algorithms with this approach. The PLADS algorithm accepts as input a set of N graph partitions either by partitioning a static graph, or fed in over time. PLADS approach uses GBAD for determining what are the normative patterns and what are the anomalous substructures.
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  Graphviz - Graph Visualization Software

Graphviz is open source graph visualization software. Graph visualization is a way of representing structural information as diagrams of abstract graphs and networks. It has important applications in networking, bioinformatics, software engineering, database and web design, machine learning, and in visual interfaces for other technical domains. The Graphviz layout programs take descriptions of graphs in a simple text language, and make diagrams in useful formats, such as images and SVG for web pages; PDF or Postscript for inclusion in other documents; or display in an interactive graph browser.
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