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Network Embedding
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Effective Model Integration Algorithm for Improving Link and Sign Prediction in Complex Networks
Controlled Deep Reinforcement Learning for Optimized Slice Placement
A Survey on Role-Oriented Network Embedding
Discrete-time Temporal Network Embedding via Implicit Hierarchical Learning in Hyperbolic Space
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts
Adversarial Robustness of Probabilistic Network Embedding for Link Prediction
Large-Scale Network Embedding in Apache Spark
Embedding Heterogeneous Networks into Hyperbolic Space Without Meta-path
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding
ImGAGN:Imbalanced Network Embedding via Generative Adversarial Graph Networks
Network embedding unveils the hidden interactions in the mammalian virome
Robust Dynamic Network Embedding via Ensembles
High Tension Lines: Predicting robustness of high-voltage power-grids to cascading failure using network embedding
Where are we in embedding spaces? A Comprehensive Analysis on Network Embedding Approaches for Recommender Systems
Independent Asymmetric Embedding for Information Diffusion Prediction on Social Networks
Multi-Aspect Temporal Network Embedding: A Mixture of Hawkes Process View
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
An Influence-based Approach for Root Cause Alarm Discovery in Telecom Networks
Hierarchical Graph Neural Networks
MUSE: Multi-faceted Attention for Signed Network Embedding
Network Embedding via Deep Prediction Model
Mutual Contrastive Learning for Visual Representation Learning
ASBERT: Siamese and Triplet network embedding for open question answering
Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes
mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding
Dynamic Network Embedding Survey
Node Proximity Is All You Need: Unified Structural and Positional Node and Graph Embedding
Progresses and Challenges in Link Prediction
Fast Graph Learning with Unique Optimal Solutions
Adversarial Attack on Network Embeddings via Supervised Network Poisoning
Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
NEMR: Network Embedding on Metric of Relation
Exact Recovery of Community Structures Using DeepWalk and Node2vec
JITuNE: Just-In-Time Hyperparameter Tuning for Network Embedding Algorithms
Heterogeneous Network Embedding for Deep Semantic Relevance Match in E-commerce Search
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