On Supervised Change Detection in Graph Streams

Abstract

Many forms of social, information, and communication network activity create large volumes of graph stream data. In such cases, it is often desirable to track interesting properties of the underlying nodes, as they change over time. These dynamic properties can often be represented in the form of time-dependent labels associated with the nodes. Dynamic changes in such node labels may be indicative of important events or patterns of activity. This paper will study the problem of differential classification in graph streams, in which we predict significant classification events; i.e. the changes in classification labels of the nodes. Different from the static collective classification problem, this approach focusses on dynamic and real-time detection of changes in node classification, as opposed to the actual classification of nodes. The differential stream classification problem can also be considered a general form of the nodecentric event detection problem, in which node labels are used in order to supervise the detection process. We present experimental results illustrating the effectiveness of our method.