Abstract: For the classification of networked data, most relational network classifiers are based on the homophily hypothesis, and the simplified processing based on the first-order Markov assumption has certain limitations. The local graph ranking algorithm ( activation spreading) is introduced into the network Bayes classifier instead of the original direct neighborhood acquisition method. The neighborhood range of nodes to be classified is appropriately expanded by setting the initial energy and the minimum energy threshold, increasing the homophily of nodes. Combined to the collective inference method of relaxation labeling, the classification
accuracy of network data is improved to a certain extent. Compared to 4 network classifiers, the experimental results show that the classification performance of the proposed method on 6 networked datasets is improved in different degrees.

Key words: artificial intelligence, classification in networked data, activation spreading, network Bayes classifier, collective inference