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Identifying outlier nodes is an important task in complex network mining. In this paper, we analyze the problem of identifying outliers in a network structure and propose an outlier measure by using the random walk distance measure and the dissimilarity index between pairs of vertices. Our method determines a “view” to the whole network for each node and infers that outliers are those nodes whose views differ significantly from majority of the nodes. Usually, outlier is detected by applying a specific criteria, for example, the farthest ones from the central node. Consequently, only one type of outliers satisfying the predefined criteria can be determined. On the other hand, our method incorporates both local and global information of the network due to random walk feature and can give more general outlier detection results. We have applied the method to artificial and real networks and some interesting results have been obtained.