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Archive of Temporal Knowledge Reasoning in Social Network and Knowledge Graph
| Date | Stars |
|---|---|
| 2026-07-31 | 460 |
| 2026-08-05 | 460 |
| 2026-08-06 | 460 |
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# 动态图表示学习、动态图分析论文汇总项目
本项目总结了动态图表示学习的有关论文，该项目在持续更新中，欢迎大家watch/star/fork！
如果大家有值得推荐的工作，可以在issue中提出要推荐的工作、论文下载链接及其工作亮点（有优秀代码实现的工作，会优先考虑在内）。项目中表述有误的部分，也可以在issue中提出。感谢！
引流：【这也是我们的工作，欢迎watch/star/fork】
社交知识图谱专题：https://github.com/jxh4945777/Social-Knowledge-Graph-Papers
目录如下：
- [动态图表示学习、动态图分析论文汇总项目](#动态图表示学习动态图分析论文汇总项目)
- [Static Graph Representation \& Analyzing Works](#static-graph-representation--analyzing-works)
- [node2vec: Scalable Feature Learning for Networks](#node2vec-scalable-feature-learning-for-networks)
- [Semi-Supervised Classification with Graph Convolutional Networks](#semi-supervised-classification-with-graph-convolutional-networks)
- [LINE: Large-scale Information Network Embedding](#line-large-scale-information-network-embedding)
- [Inductive representation learning on large graphs](#inductive-representation-learning-on-large-graphs)
- [Graph Attention Networks](#graph-attention-networks)
- [Anonymous Walk Embeddings](#anonymous-walk-embeddings)
- [Dynamic Graph Representation](#dynamic-graph-representation)
- [Representation Learning for Dynamic Graphs: A Survey](#representation-learning-for-dynamic-graphs-a-survey)
- [Foundations and modelling of dynamic networks using Dynamic Graph Neural Networks: A survey](#foundations-and-modelling-of-dynamic-networks-using-dynamic-graph-neural-networks-a-survey)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](#a-survey-on-knowledge-graphs-representation-acquisition-and-applications)
- [Temporal Link Prediction: A Survey](#temporal-link-prediction-a-survey)
- [Temporal Networks](#temporal-networks)
- [Evolutionary Network Analysis: A Survey](#evolutionary-network-analysis-a-survey)
- [Motifs in Temporal Networks](#motifs-in-temporal-networks)
- [动态网络模式挖掘方法及其应用](#动态网络模式挖掘方法及其应用)
- [New Works of Dynamic Graph Representation (Updating)](#new-works-of-dynamic-graph-representation-updating)
- [Link Prediction with Spatial and Temporal Consistency in Dynamic Networks](#link-prediction-with-spatial-and-temporal-consistency-in-dynamic-networks)
- [Deep Coevolutionary Network: Embedding User and Item Features for Recommendation](#deep-coevolutionary-network-embedding-user-and-item-features-for-recommendation)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](#know-evolve-deep-temporal-reasoning-for-dynamic-knowledge-graphs)
- [NEURAL RELATIONAL INFERENCE FOR INTERACTING SYSTEMS](#neural-relational-inference-for-interacting-systems)
- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](#spatio-temporal-graph-convolutional-networks-a-deep-learning-framework-for-traffic-forecasting)
- [Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding](#dynamic-network-embedding--an-extended-approach-for-skip-gram-based-network-embedding)
- [Embedding Temporal Network via Neighborhood Formation](#embedding-temporal-network-via-neighborhood-formation)
- [Continuous-Time Dynamic Network Embeddings](#continuous-time-dynamic-network-embeddings)
- [Dynamic Network Embedding by Modeling Triadic Closure Process](#dynamic-network-embedding-by-modeling-triadic-closure-process)
- [NetWalk: A Flexible Deep Embedding Approach for Anomaly Detection in Dynamic Networks](#netwalk-a-flexible-deep-embedding-approach-for-anomaly-detection-in-dynamic-networks)
- [Dynamic graph convolutional networks](#dynamic-graph-convolutional-networks)
- [Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs](#spatio-temporal-attentive-rnn-for-node-classification-in-temporal-attributed-graphs)
- [DYREP: LEARNING REPRESENTATIONS OVER DYNAMIC GRAPHS](#dyrep-learning-representations-over-dynamic-graphs)
- [Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models](#learning-to-reprExcerpt of 58,019 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:b7c4d7016128de58, desc:knowledge graph