94 research outputs found
Node Classification in Social Networks
When dealing with large graphs, such as those that arise in the context of
online social networks, a subset of nodes may be labeled. These labels can
indicate demographic values, interest, beliefs or other characteristics of the
nodes (users). A core problem is to use this information to extend the labeling
so that all nodes are assigned a label (or labels). In this chapter, we survey
classification techniques that have been proposed for this problem. We consider
two broad categories: methods based on iterative application of traditional
classifiers using graph information as features, and methods which propagate
the existing labels via random walks. We adopt a common perspective on these
methods to highlight the similarities between different approaches within and
across the two categories. We also describe some extensions and related
directions to the central problem of node classification.Comment: To appear in Social Network Data Analytics (Springer) Ed. Charu
Aggarwal, March 201
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
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