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WWW2020 图相关论文集
“WWW(International World Wide Web Conferences,国际万维网大会),由国际万维网会议指导委员会主办,是CCF A类会议。
“全部收录论文列表:https://dblp.uni-trier.de/db/conf/www/www2020.html
01
Full Paper
图卷积
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Unsupervised Domain Adaptive Graph Convolutional Networks -
A Generic Edge-Empowered Graph Convolutional Network via Node-Edge Mutual Enhancement
异构图
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Task-Oriented Genetic Activation for Large-Scale Complex Heterogeneous Graph Embedding -
MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding
图注意力模型
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Towards Fine-grained Flow Forecasting: A Graph Attention Approach for Bike Sharing Systems -
Graph Attention Topic Modeling Network -
High Quality Candidate Generation and Sequential Graph Attention Network for Entity Linking
图特征学习
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Graph Representation Learning via Graphical Mutual Information Maximization -
Graph Enhanced Representation Learning for News Recommendation
图对抗攻击
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Adversarial Attacks on Graph Neural Networks via Node Injections: A Hierarchical Reinforcement Learning Approach
图生成模型
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GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation
图聚类
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Clustering in graphs and hypergraphs with categorical edge labels
动态图
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Dynamic Graph Convolutional Networks for Entity Linking
链路预测
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Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction
知识图谱
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Relation Adversarial Network for Low Resource Knowledge Graph Completion -
Reinforced Negative Sampling over Knowledge Graph for Recommendation -
ASER: A Large-scale Eventuality Knowledge Graph -
Keyword Search over Knowledge Graphs via Static and Dynamic Hub Labelings -
Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning -
What is Normal, What is Strange, and What is Missing in a Knowledge Graph: Unified Characterization via Inductive Summarization -
Complex Factoid Question Answering with a Free-Text Knowledge Graph -
Adaptive Low-level Storage of Very Large Knowledge Graphs -
Collective Multi-type Entity Alignment Between Knowledge Graphs
时空图
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Traffic Flow Prediction via Spatial Temporal Graph Neural Network
图生成
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GraphGen: A Scalable Approach to Domain-agnostic Labeled Graph Generation
GNN
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Learning to Hash with Graph Neural Networks for Recommender Systems -
TaxoExpan: Self-supervised Taxonomy Expansion with Position-Enhanced Graph Neural Network
扩展
推荐系统
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Weakly Supervised Attention for Hashtag Recommendation using Graph Data -
Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit
其他
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Smaller, Faster & Lighter KNN Graph Constructions -
Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks -
Finding large balanced subgraphs in signed networks -
Traveling the token world: A graph analysis of Ethereum ERC20 token ecosystem -
Flowless: Extracting Densest Subgraphs Without Flow Computations -
Beyond Rank-1: Discovering Rich Community Structure in Multi-Aspect Graphs -
Searching for polarization in signed graphs: a local spectral approach -
Power-Law Graphs Have Minimal Scaling of Kemeny Constant for Random Walks
02
Short Paper
异构图
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Heterogeneous Graph Transformer
链路预测
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Searching for Embeddings in a Haystack: Link Prediction on Knowledge Graphs with Subgraph Pruning -
Continuous-Time Link Prediction via Temporal Dependent Graph Neural Network
图特征学习
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Graph Enhanced Representation Learning for News Recommendation -
Learning Temporal Interaction Graph Embedding via Coupled Memory Networks
知识图谱
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Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs -
Graph-Query Suggestions for Knowledge Graph Exploration
图聚类
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One2Multi Graph Autoencoder for Multi-view Graph Clustering
超图
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How Much and When Do We Need Higher-order Informationin Hypergraphs? A Case Study on Hyperedge Prediction
图比较
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Just SLaQ When You Approximate: Accurate Spectral Distances for Web-Scale Graphs
行为预测
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Beyond Clicks: Modeling Multi-Relational Item Graph for Session-Based Target Behavior Prediction
图池化方法
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Structure-Feature based Graph Self-adaptive Pooling
其他
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Higher-Order Label Homogeneity and Spreading in Graphs -
Deconstruct Densest Subgraphs -
Using Cliques with Higher-order Spectral Embeddings Improves Graph Visualizations
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