Adaptive Neural Network for Node Classification in Dynamic Networks

Publication Date: 11/11/2019

Event: The 19th IEEE International Conference on Data Mining (ICDM 2019)

Reference: pp. 1402-1407, 2019

Authors: Dongkuan Xu, NEC Laboratories America, Inc.; Pennsylvania State University; Wei Cheng, NEC Laboratories America, Inc.; Dongsheng Luo, Pennsylvania State University; Yameng Gu, Pennsylvania State University; Xiao Liu, Pennsylvania State University; Jingchao Ni, NEC Laboratories America, Inc.; Bo Zong, NEC Laboratories America, Inc.; Haifeng Chen, NEC Laboratories America, Inc.; Xiang Zhang, Pennsylvania State University

Abstract: Given a network with the labels for a subset of nodes, transductive node classification targets to predict the labels for the remaining nodes in the network. This technique has been used in a variety of applications such as voxel functionality detection in brain network and group label prediction in social network. Most existing node classification approaches are performed in static networks. However, many real-world networks are dynamic and evolve over time. The dynamics of both node attributes and network topology jointly determine the node labels. In this paper, we study the problem of classifying the nodes in dynamic networks. The task is challenging for three reasons. First, it is hard to effectively learn the spatial and temporal information simultaneously. Second, the network evolution is complex. The evolving patterns lie in both node attributes and network topology. Third, for different networks or even different nodes in the same network, the node attributes, the neighborhood node representations and the network topology usually affect the node labels differently, it is desirable to assess the relative importance of different factors over evolutionary time scales. To address the challenges, we propose AdaNN, an adaptive neural network for transductive node classification. AdaNN learns node attribute information by aggregating the node and its neighbors, and extracts network topology information with a random walk strategy. The attribute information and topology information are further fed into two connected gated recurrent units to learn the spatio-temporal contextual information. Additionally, a triple attention module is designed to automatically model the different factors that influence the node representations. AdaNN is the first node classification model that is adaptive to different kinds of dynamic networks. Extensive experiments on real datasets demonstrate the effectiveness of AdaNN.

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