Abstract
This work develops a broad learning method for link prediction on dynamic heterogeneous information networks. While existing works have primarily focused on dynamic homogeneous networks or static heterogeneous networks. As such, the existing methods can be suboptimal for link prediction on dynamic heterogeneous information networks.
In this paper, we try to study the problem of link prediction combining dynamic networks and heterogeneous networks. However, none of the existing works has paid special attention to connect these two kinds of network data. To tackle this challenge, we propose a new broad learning-based method named HA-LSTM, short for Hierarchical Attention Long-Short Time Memory to address this problem on dynamic heterogeneous information networks. Firstly, we employ the Graph Convolutional Network (GCN) to extract the feature from Heterogeneous Information Networks (HINs). Then, we utilize a broad learning and attention based framework to fuse and extract the information among HINs broadly over timestamps. Finally, the link prediction in time-dimension by employing LSTM could be performed. We conduct extensive experiments on several real dynamic heterogeneous information networks covering the task of link prediction. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our HA-LSTM method.
This work is supported by the Initial Scientific Research Fund of Introduced Talents in Anhui Polytechnic University (No. 2017YQQ015), Pre-research Project of National Natural Science Foundation of China (No. 2019yyzr03) and National Natural Science Foundation of China Youth Fund (No. 61300170 and No. 61902001).
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Kong, C., Li, H., Zhang, L., Zhu, H., Liu, T. (2019). Link Prediction on Dynamic Heterogeneous Information Networks. In: Tagarelli, A., Tong, H. (eds) Computational Data and Social Networks. CSoNet 2019. Lecture Notes in Computer Science(), vol 11917. Springer, Cham. https://doi.org/10.1007/978-3-030-34980-6_36
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DOI: https://doi.org/10.1007/978-3-030-34980-6_36
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