DeepWalk: Online Learning of Social Representations

Abstract

We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.

10 pages, 5 figures, 4 tables

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• Modularity and community structure in networks
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• Position-Sensing Graph Neural Networks: Proactively Learning Nodes Relative Positions
• A Deep Latent Space Model for Graph Representation Learning
• Deep Contrastive Multiview Network Embedding
• Bingo: Radix-based Bias Factorization for Random Walk on Dynamic Graphs
• JPEC: A Novel Graph Neural Network for Competitor Retrieval in Financial Knowledge Graphs
• TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
• Enhancing Unsupervised Graph Few-shot Learning via Set Functions and Optimal Transport
• Soccer Team Vectors
• ${\text{H}}^2\text{TNE}$: Temporal Heterogeneous Information Network Embedding in Hyperbolic Spaces
• Identification of Device Dependencies Using Link Prediction
• ECHO: Encoding Communities via High-order Operators
• Transfer Learning for Node Regression Applied to Spreading Prediction
• Understanding the Design Principles of Link Prediction in Directed Settings
• DINE: A Framework for Deep Incomplete Network Embedding
• RNE: A Scalable Network Embedding for Billion-scale Recommendation
• Expanding Semantic Knowledge for Zero-shot Graph Embedding
• Effective Graph and Rank-based Contextual Embeddings for Textual and Multimedia Data
• Classification with Costly Features in Hierarchical Deep Sets
• Methodology for Identifying Social Groups within a Transactional Graph
• QWalkVec: Node Embedding by Quantum Walk
• Focus Where It Matters: Graph Selective State Focused Attention Networks
• From Anchors to Answers: A Novel Node Tokenizer for Integrating Graph Structure into Large Language Models
• Representation Learning on Large Non-Bipartite Transaction Networks using GraphSAGE
• A Generative Framework for Predictive Modeling of Multiple Chronic Conditions Using Graph Variational Autoencoder and Bandit-Optimized Graph Neural Network
• Symmetrical SyncMap for Imbalanced General Chunking Problems
• Link Prediction in Bipartite Networks
• Knowledge Graph Completion using Structural and Textual Embeddings
• Conversation-Based Multimodal Abuse Detection Through Text and Graph Embeddings
• Block-Approximated Exponential Random Graphs
• A Text-based Approach For Link Prediction on Wikipedia Articles
• A Literature Review of Recent Graph Embedding Techniques for Biomedical Data
• FeatureNorm: L2 Feature Normalization for Dynamic Graph Embedding
• Automated Feature-Topic Pairing: Aligning Semantic and Embedding Spaces in Spatial Representation Learning
• Graph Embedding via Diffusion-Wavelets-Based Node Feature Distribution Characterization
• Attributed Graph Modeling with Vertex Replacement Grammars
• FULL-W2V: Fully Exploiting Data Reuse for W2V on GPU-Accelerated Systems
• Label-Aware Graph Convolutional Networks
• A Structural Feature-Based Approach for Comprehensive Graph Classification
• SGPT: Few-Shot Prompt Tuning for Signed Graphs
• Evaluating link prediction: New perspectives and recommendations
• A Framework of Transferring Structures Across Large-scale Information Networks
• Generic Multimodal Spatially Graph Network for Spatially Embedded Network Representation Learning
• Leap: Inductive Link Prediction via Learnable TopologyAugmentation
• Drug-disease networks and drug repurposing
• Constructing Graph Node Embeddings via Discrimination of Similarity Distributions
• A Survey on GNN-based Link Prediction: Techniques, Applications, and Challenges
• Context-Aware Interpretable Representations for Retrieval and Graph Convolutional Network Classification
• Trivial Graph Features and Classical Learning are Enough to Detect Random Anomalies
• LLMs Between the Nodes: Community Discovery Beyond Vectors
• Scalable Deep Metric Learning on Attributed Graphs
• Graph Size-imbalanced Learning with Energy-guided Structural Smoothing
• Data-Driven Self-Supervised Graph Representation Learning
• mQAPViz: A divide-and-conquer multi-objective optimization algorithm to compute large data visualizations
• Self-Supervised Contrastive Graph Clustering Network via Structural Information Fusion
• Revisiting Local PageRank Estimation on Undirected Graphs: Simple and Optimal
• Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
• Graph Embedding Augmented Skill Rating System
• One Node at a Time: Node-Level Network Classification