24,103 research outputs found
An Attention-based Collaboration Framework for Multi-View Network Representation Learning
Learning distributed node representations in networks has been attracting
increasing attention recently due to its effectiveness in a variety of
applications. Existing approaches usually study networks with a single type of
proximity between nodes, which defines a single view of a network. However, in
reality there usually exists multiple types of proximities between nodes,
yielding networks with multiple views. This paper studies learning node
representations for networks with multiple views, which aims to infer robust
node representations across different views. We propose a multi-view
representation learning approach, which promotes the collaboration of different
views and lets them vote for the robust representations. During the voting
process, an attention mechanism is introduced, which enables each node to focus
on the most informative views. Experimental results on real-world networks show
that the proposed approach outperforms existing state-of-the-art approaches for
network representation learning with a single view and other competitive
approaches with multiple views.Comment: CIKM 201
Transfer Learning across Networks for Collective Classification
This paper addresses the problem of transferring useful knowledge from a
source network to predict node labels in a newly formed target network. While
existing transfer learning research has primarily focused on vector-based data,
in which the instances are assumed to be independent and identically
distributed, how to effectively transfer knowledge across different information
networks has not been well studied, mainly because networks may have their
distinct node features and link relationships between nodes. In this paper, we
propose a new transfer learning algorithm that attempts to transfer common
latent structure features across the source and target networks. The proposed
algorithm discovers these latent features by constructing label propagation
matrices in the source and target networks, and mapping them into a shared
latent feature space. The latent features capture common structure patterns
shared by two networks, and serve as domain-independent features to be
transferred between networks. Together with domain-dependent node features, we
thereafter propose an iterative classification algorithm that leverages label
correlations to predict node labels in the target network. Experiments on
real-world networks demonstrate that our proposed algorithm can successfully
achieve knowledge transfer between networks to help improve the accuracy of
classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201
Similarity-Based Classification in Partially Labeled Networks
We propose a similarity-based method, using the similarity between nodes, to
address the problem of classification in partially labeled networks. The basic
assumption is that two nodes are more likely to be categorized into the same
class if they are more similar. In this paper, we introduce ten similarity
indices, including five local ones and five global ones. Empirical results on
the co-purchase network of political books show that the similarity-based
method can give high accurate classification even when the labeled nodes are
sparse which is one of the difficulties in classification. Furthermore, we find
that when the target network has many labeled nodes, the local indices can
perform as good as those global indices do, while when the data is sparce the
global indices perform better. Besides, the similarity-based method can to some
extent overcome the unconsistency problem which is another difficulty in
classification.Comment: 13 pages,3 figures,1 tabl
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