Graph-based Security and Privacy Analytics via Collective Classification with Joint Weight Learning and Propagation

Understand

Many security and privacy problems can be modeled as a graph classification problem, where nodes in the graph are classified by collective classification simultaneously.

• State-of-the-art collective classification methods for such graph-based security and privacy analytics follow the following paradigm: assign weights to edges of the graph, iteratively propagate reputation scores of nodes among the weighted graph, and use the final reputation scores to classify nodes in the graph.
• The key challenge is to assign edge weights such that an edge has a large weight if the two corresponding nodes have the same label, and a small weight otherwise.
• Although collective classification has been studied and applied for security and privacy problems for more than a decade, how to address this challenge is still an open question.