2015 HeterogeneousNetworkEmbeddingvi
- (Chang et al., 2015) ⇒ Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, and Thomas S. Huang. (2015). “Heterogeneous Network Embedding via Deep Architectures.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783296
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Cited By
- http://scholar.google.com/scholar?q=%222015%22+Heterogeneous+Network+Embedding+via+Deep+Architectures
- http://dl.acm.org/citation.cfm?id=2783258.2783296&preflayout=flat#citedby
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Author Keywords
- Cross-domain knowledge propagation; data mining; deep learning; dimensionality reduction; feature learning; heterogeneous embedding; network embedding
Abstract
Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination of heterogeneous contents and structures. The creation of a multidimensional embedding of such data opens the door to the use of a wide variety of off-the-shelf mining techniques for multidimensional data. Despite the importance of this problem, limited efforts have been made on embedding a network of scalable, dynamic and heterogeneous data. In such cases, both the content and linkage structure provide important cues for creating a unified feature representation of the underlying network. In this paper, we design a deep embedding algorithm for networked data. A highly nonlinear multi-layered embedding function is used to capture the complex interactions between the heterogeneous data in a network. Our goal is to create a multi-resolution deep embedding function, that reflects both the local and global network structures, and makes the resulting embedding useful for a variety of data mining tasks. In particular, we demonstrate that the rich content and linkage information in a heterogeneous network can be captured by such an approach, so that similarities among cross-modal data can be measured directly in a common embedding space. Once this goal has been achieved, a wide variety of data mining problems can be solved by applying off-the-shelf algorithms designed for handling vector representations. Our experiments on real-world network datasets show the effectiveness and scalability of the proposed algorithm as compared to the state-of-the-art embedding methods.
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Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
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2015 HeterogeneousNetworkEmbeddingvi | Charu C. Aggarwal Jiliang Tang Shiyu Chang Wei Han Guo-Jun Qi Thomas S. Huang | Heterogeneous Network Embedding via Deep Architectures | 10.1145/2783258.2783296 | 2015 |