30,434 research outputs found
AAANE: Attention-based Adversarial Autoencoder for Multi-scale Network Embedding
Network embedding represents nodes in a continuous vector space and preserves
structure information from the Network. Existing methods usually adopt a
"one-size-fits-all" approach when concerning multi-scale structure information,
such as first- and second-order proximity of nodes, ignoring the fact that
different scales play different roles in the embedding learning. In this paper,
we propose an Attention-based Adversarial Autoencoder Network Embedding(AAANE)
framework, which promotes the collaboration of different scales and lets them
vote for robust representations. The proposed AAANE consists of two components:
1) Attention-based autoencoder effectively capture the highly non-linear
network structure, which can de-emphasize irrelevant scales during training. 2)
An adversarial regularization guides the autoencoder learn robust
representations by matching the posterior distribution of the latent embeddings
to given prior distribution. This is the first attempt to introduce attention
mechanisms to multi-scale network embedding. Experimental results on real-world
networks show that our learned attention parameters are different for every
network and the proposed approach outperforms existing state-of-the-art
approaches for network embedding.Comment: 8 pages, 5 figure
Adversarial Deep Network Embedding for Cross-network Node Classification
In this paper, the task of cross-network node classification, which leverages
the abundant labeled nodes from a source network to help classify unlabeled
nodes in a target network, is studied. The existing domain adaptation
algorithms generally fail to model the network structural information, and the
current network embedding models mainly focus on single-network applications.
Thus, both of them cannot be directly applied to solve the cross-network node
classification problem. This motivates us to propose an adversarial
cross-network deep network embedding (ACDNE) model to integrate adversarial
domain adaptation with deep network embedding so as to learn network-invariant
node representations that can also well preserve the network structural
information. In ACDNE, the deep network embedding module utilizes two feature
extractors to jointly preserve attributed affinity and topological proximities
between nodes. In addition, a node classifier is incorporated to make node
representations label-discriminative. Moreover, an adversarial domain
adaptation technique is employed to make node representations
network-invariant. Extensive experimental results demonstrate that the proposed
ACDNE model achieves the state-of-the-art performance in cross-network node
classification
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