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
Benchmarks
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Libraries
Use these libraries to find Network Embedding models and implementations
arXiv Astro-Ph
Netzschleuder
Most implemented papers
Sub-graph Contrast for Scalable Self-Supervised Graph Representation Learning
yzjiao/Subg-Con
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Instead of learning on the complete input graph data, with a novel data augmentation strategy, \textsc{Subg-Con} learns node representations through a contrastive loss defined based on subgraphs sampled from the original graph instead.
Robust Dynamic Network Embedding via Ensembles
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
Name Disambiguation in Anonymized Graphs using Network Embedding
In real-world, our DNA is unique but many people share names.
Font Size: Community Preserving Network Embedding
While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored.
Enhancing Network Embedding with Auxiliary Information: An Explicit Matrix Factorization Perspective
In this paper, we take a matrix factorization perspective of network embedding, and incorporate structure, content and label information of the network simultaneously.
Learning Role-based Graph Embeddings
Random walks are at the heart of many existing network embedding methods.
Fast Sequence Based Embedding with Diffusion Graphs
A graph embedding is a representation of the vertices of a graph in a low dimensional space, which approximately preserves proper-ties such as distances between nodes.
Fusing Structure and Content via Non-negative Matrix Factorization for Embedding Information Networks
It is not straightforward to integrate the content of each node in the current state-of-the-art network embedding methods.
Billion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently.
Multi-Level Network Embedding with Boosted Low-Rank Matrix Approximation
As opposed to manual feature engineering which is tedious and difficult to scale, network representation learning has attracted a surge of research interests as it automates the process of feature learning on graphs.
