Graffiti: node labeling in heterogeneous networks

We introduce a multi-label classification model and algorithm for labeling heterogeneous networks, where nodes belong to different types and different types have different sets of classification labels. We present a graph-based approach which models the mutual influence between nodes in the network as a random walk. When viewing class labels as "colors", the random surfer is"spraying"different node types with different color palettes; hence the name Graffiti. We demonstrate the performance gains of our method by comparing it to three state-of-the-art techniques for graph-based classification. Categories and Subject Descriptors I.5.2 [Pattern Recognition]: Classifier design and evaluation; G.3 [Probability and statistics]: Markov processes General Terms Graph Classification, Link Analysis

• Type: Conference
• Year: 2009
• Where: WWW
• Authors: Ralitsa Angelova, Gjergji Kasneci, Fabian M. Suchanek, Gerhard Weikum
• Keywords: Classification Labels, Graph-based Classification, Internet Technology, Terms Graph Classification, WWW 2009