Network Embedding Theories, Methods and Applications

Description

Preface Acknowledgments Introduction to Network Embedding The Basics of Network Embedding Background The Rising of Network Embedding The Evaluation of Network Embedding Node Classification Link Prediction Node Clustering Network Embedding for General Graphs Representative Methods Early Work (Early 2000s – Early 2010s) Modern Work (After 2014) Theory: A Unified Network Embedding Framework K-Order Proximity Network Embedding Framework Observations Method: Network Embedding Update (NEU) Problem Formalization Approximation Algorithm Empirical Analysis Datasets Baselines and Experimental Settings Multi-Label Classification Link Prediction Experimental Results Analysis Further Reading Network Embedding with Additional Information Network Embedding for Graphs with Node Attributes Overview Method: Text-Associated DeepWalk Low-Rank Matrix Factorization Text-Associated DeepWalk (TADW) Complexity Analysis Empirical Analysis Datasets TADW Settings Baseline Methods Classifiers and Experiment Setup Experimental Results and Analysis Case Study Further Reading Revisiting Attributed Network Embedding: A GCN-Based Perspective GCN-Based Network Embedding Graph Convolutional Network (GCN) Attributed Network Embedding based on GCN Discussions Method: Adaptive Graph Encoder Problem Formalization Overall Framework Laplacian Smoothing Filter Adaptive Encoder Empirical Analysis Datasets Baseline Methods Evaluation Metrics and Parameter Settings Node Clustering Results Link Prediction Results GAE vs. LS+RA Ablation Study Selection of k Visualization Further Reading Network Embedding for Graphs with Node Contents Overview Method: Context-Aware Network Embedding Problem Formalization Overall Framework Structure-Based Objective Text-Based Objective Context-Free Text Embedding Context-Aware Text Embedding Optimization of CANE Empirical Analysis Datasets Baselines Evaluation Metrics and Experimental Settings Link Prediction Node Classification Case Study Further Reading Network Embedding for Graphs with Node Labels Overview Method: Max-Margin DeepWalk Problem Formalization DeepWalk Based on Matrix Factorization Max-Margin DeepWalk Optimization of MMDW Empirical Analysis Datasets and Experiment Settings Baseline Methods Experimental Results and Analysis Visualization Further Reading Network Embedding with Different Characteristics Network Embedding for Community-Structured Graphs Overview Method: Community-Enhanced NRL Problem Formalization DeepWalk Community-Enhanced DeepWalk Empirical Analysis Datasets Baseline Methods Parameter Settings and Evaluation Metrics Node Classification Link Prediction Community Detection Visualizations of Detected Communities Further Reading Network Embedding for Large-Scale Graphs Overview Method: COmpresSIve Network Embedding (COSINE) Problem Definition Graph Partitioning Group Mapping Group Aggregation Objective Function and Optimization Empirical Analysis Datasets Baselines and Experimental Settings Link Prediction Multi-Label Classification Scalability Time Efficiency Different Partitioning Algorithms Further Reading Network Embedding for Heterogeneous Graphs Overview Method: Relation Structure-Aware HIN Embedding Problem Formalization Data Observations Basic Idea Different Models for ARs and IRs A Unified Model for HIN Embedding Empirical Analysis Datasets Baseline Methods Parameter Settings Node Clustering Link Prediction Multi-Class Classification Comparison of Variant Models Visualization Further Reading Network Embedding Applications Network Embedding for Social Relation Extraction Overview Method: TransNet Problem Formalization Translation Mechanism Edge Representation Construction Overall Architecture Prediction Empirical Analysis Datasets Baselines Evaluation Metrics and Parameter Settings Results and Analysis Comparison on Labels Case Study Further Reading Network Embedding for Recommendation Systems on LBSNs Overview Method: Joint Network and Trajectory Model (JNTM) Problem Formalization Modeling the Construction of the Social Network Modeling the Generation of the Mobile Trajectories The Joint Model Parameter Learning Empirical Analysis Data Collection Evaluation Tasks and Baselines Experimental Results on Next-Location Recommendation Experimental Results on Friend Recommendation Further Reading Network Embedding for Information Diffusion Prediction Overview Method: Neural Diffusion Model (NDM) Problem Formalization Model Assumptions Extracting Active Users with Attention Mechanism Unifying Active User Embeddings for Prediction with Convolutional Network Overall Architecture, Model Details, and Learning Algorithms Empirical Analysis Datasets Baselines Hyperparameter Settings Microscopic Diffusion Prediction Benefits from Network Embeddings Interpretability Further Reading Outlook for Network Embedding Future Directions of Network Embedding Network Embedding Based on Advanced Techniques Network Embedding in More Fine-Grained Scenarios Network Embedding with Better Interpretability Network Embedding for Applications Bibliography Authors' Biographies Blank Page