document: 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 .
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