Structural Deep Network Embedding
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Summary
This paper proposes a Structural Deep Network Embedding method, namely SDNE, which first proposes a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non- linear network structure and exploits the first-order and second-order proximity jointly to preserve the network structure.
- Type
- article
- Published
- 2016-08-13
- Cited by
- 2,825
- References
- 38
- OpenAlex
- https://openalex.org/W2393319904
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:207238964
Keywords
Computer science, Embedding, Artificial intelligence, Exploit, Network science
References
- LINE: Large-scale Information Network Embedding
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- Learning Deep Architectures for AI
- Structure preserving embedding
- GraRep: Learning Graph Representations with Global Structural Information
- Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
- Structure of growing social networks.
- Scalable learning of collective behavior based on sparse social dimensions
- Graph evolution: Densification and shrinking diameters
- A non-IID Framework for Collaborative Filtering with Restricted Boltzmann Machines
- LIBLINEAR: A Library for Large Linear Classification
- A new approach to interdomain routing based on secure multi-party computation
- A matrix factorization technique with trust propagation for recommendation in social networks
- A Fast Learning Algorithm for Deep Belief Nets
- Two-Layer Multiple Kernel Learning
- DeepWalk: online learning of social representations
Cited by
- Node classification over bipartite graphs through projection
- A General Framework for Content-enhanced Network Representation Learning
- From Node Embedding To Community Embedding
- Heterogeneous Information Network Embedding for Meta Path based Proximity
- Reducing uncertainty of dynamic heterogeneous information networks: a fusing reconstructing approach
- Label Informed Attributed Network Embedding
- A Graph Summarization: A Survey
- Interaction Network Representations for Human Behavior Prediction
- On spectral analysis of directed signed graphs
- Distributed Representations of Subgraphs
- Fast, Warped Graph Embedding: Unifying Framework and One-Click Algorithm
- Learning network representations
- Relational Deep Learning: A Deep Latent Variable Model for Link Prediction
- Deep Collective Inference
- Community Preserving Network Embedding
- Semantic Proximity Search on Heterogeneous Graph by Proximity Embedding
- struc2vec: Learning Node Representations from Structural Identity
- Generic frameworks for interactive personalized interesting pattern discovery
- Graph Embedding Techniques, Applications, and Performance: A Survey
- Active Learning for Graph Embedding
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