Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks

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Author(s)
Trung, Huynh Thanh
Vinh, Tong Van
Tam, Nguyen Thanh
Yin, Hongzhi
Weidlich, Matthias
Nguyen, Quoc Viet Hung
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2020
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Dallas, USA

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Abstract

Network alignment is the problem of pairing nodes between two graphs such that the paired nodes are structurally and semantically similar. A well-known application of network alignment is to identify which accounts in different social networks belong to the same person. Existing alignment techniques, however, lack scalability, cannot incorporate multi-dimensional information without training data, and are limited in the consistency constraints enforced by an alignment. In this paper, we propose a fully unsupervised network alignment framework based on a multi-order embedding model. The model learns the embeddings of each node using a graph convolutional neural representation, which we prove to satisfy consistency constraints. We further design a data augmentation method and a refinement mechanism to make the model adaptive to consistency violations and noise. Extensive experiments on real and synthetic datasets show that our model outperforms state-of-the-art alignment techniques. We also demonstrate the robustness of our model against adversarial conditions, such as structural noises, attribute noises, graph size imbalance, and hyper-parameter sensitivity.

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9131

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2020 IEEE 36th International Conference on Data Engineering (ICDE)

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© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Networking and communications

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Thanh Trung, H; Van Vinh, T; Tam, NT; Yin, H; Weidlich, M; Nguyen, QVH, Adaptive Network Alignment with Unsupervised and Multi-order Convolutional Networks, 2020 IEEE 36th International Conference on Data Engineering (ICDE), 2020, pp. 85-96