Network Embedding
152 papers with code • 0 benchmarks • 4 datasets
Network Embedding, also known as "Network Representation Learning", is a collective term for techniques for mapping graph nodes to vectors of real numbers in a multidimensional space. To be useful, a good embedding should preserve the structure of the graph. The vectors can then be used as input to various network and graph analysis tasks, such as link prediction
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arXiv Astro-Ph
Netzschleuder
Latest papers with no code
Source-Aware Embedding Training on Heterogeneous Information Networks
Heterogeneous information networks (HINs) have been extensively applied to real-world tasks, such as recommendation systems, social networks, and citation networks.
Graph-Level Embedding for Time-Evolving Graphs
We evaluate our proposed model on five publicly available datasets for the task of temporal graph similarity ranking, and our model outperforms baseline methods.
Modeling Dynamic Heterogeneous Graph and Node Importance for Future Citation Prediction
To cope with this problem, we propose a Dynamic heterogeneous Graph and Node Importance network (DGNI) learning framework, which fully leverages the dynamic heterogeneous graph and node importance information to predict future citation trends of newly published papers.
Semantic Random Walk for Graph Representation Learning in Attributed Graphs
Different from existing embedding methods that treat the incorporation of graph structure and semantic as the simple combination of two optimization objectives, we propose a novel semantic graph representation (SGR) method to formulate the joint optimization of the two heterogeneous sources into a common high-order proximity based framework.
Zoo Guide to Network Embedding
Networks have provided extremely successful models of data and complex systems.
Space-Invariant Projection in Streaming Network Embedding
Newly arriving nodes in dynamics networks would gradually make the node embedding space drifted and the retraining of node embedding and downstream models indispensable.
Cross Version Defect Prediction with Class Dependency Embeddings
Our approach uses network embedding techniques to leverage CDN information without having to build the metrics manually.
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives
Our proposal performs better in terms of name disambiguation accuracy compared with baselines and the ablation experiments demonstrate the improvement by feature selection and the meta-path level attention in our method.
Recommending on graphs: a comprehensive review from a data perspective
Recent advances in graph-based learning approaches have demonstrated their effectiveness in modelling users' preferences and items' characteristics for Recommender Systems (RSS).
A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging
To address that, we propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features.
