## 1 Literature Review

Title	Year	Publication	Materials	BibTexRef
Deep Learning on Graphs: A Survey	2020	TKDE	[PDF]	[1]

2.Graph Neural Network

3. Graph Embedding

4. Dynamic Graphs Embedding

Title	Year	Publication	Main idea	Materials	BibTexRef
Continuous-Time Dynamic Network Embeddings	2018	WWW	CTDNE	[PDF]	[2]
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks	2018	AAAI	DepthLGP	[PDF]	[3]
Dynamic Network Embedding by Modeling Triadic Closure Process	2018	AAAI	DynamicTriad	[PDF]	[4]
TIMERS: Error-Bounded SVD Restart on Dynamic Networks	2018	AAAI	TIMERS	[PDF]	[5]
Dynamic Network Embedding :An Extended Approach for Skip-gram based Network Embedding	2018	IJCAI	DNE/extend LINE SGNE	[PDF]	[6]
Embedding Temporal Network via Neighborhood Formation	2018	KDD	Hawkes process based Temporal Network Embedding (HTNE) method	[PDF]	[7]
Temporal network embedding with micro-and macro-dynamics	2019	CIKM	$M^2DNE$/M2DNE	[PDF]	[8]
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs	2020	AAAI	EvolveGCN	[PDF] [CODE]	[9]
Temporal graph networks for deep learning on dynamic graphs	2020	arxiv	TGN	[PDF]	[10]
Spatio-Temporal Attentive RNN for Node Classiicationin Temporal Attributed Graphs	2019	IJCAI	STAR	[PDF]	[11]
DynGEM: Deep embedding method for dynamic graphs	2018	arxiv	DynGEM	[PDF]	[12]
Graph2seq: Scalable learning dynamics for graphs	2018	arxiv	Graph2seq	[PDF]	[13]
APAN: Asynchronous Propagation Atention Network forReal-time Temporal Graph Embedding	2020	arxiv	APAN	[PDF]	[14]

5.Application

Node Classification

Link Prediction

Anomaly Detection

References

[1]

@article{zhang2020deep,
  title={Deep learning on graphs: A survey},
  author={Zhang, Ziwei and Cui, Peng and Zhu, Wenwu},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2020},
  publisher={IEEE}
}

[2]

@inproceedings{nguyen2018continuous,
  title={Continuous-time dynamic network embeddings},
  author={Nguyen, Giang Hoang and Lee, John Boaz and Rossi, Ryan A and Ahmed, Nesreen K and Koh, Eunyee and Kim, Sungchul},
  booktitle={Companion Proceedings of the The Web Conference 2018},
  pages={969--976},
  year={2018}
}

[3]

@inproceedings{ma2018depthlgp,
  title={Depthlgp: learning embeddings of out-of-sample nodes in dynamic networks},
  author={Ma, Jianxin and Cui, Peng and Zhu, Wenwu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}

[4]

@inproceedings{zhou2018dynamic,
  title={Dynamic network embedding by modeling triadic closure process},
  author={Zhou, Lekui and Yang, Yang and Ren, Xiang and Wu, Fei and Zhuang, Yueting},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}

[5]

@inproceedings{zhang2018timers,
  title={Timers: Error-bounded svd restart on dynamic networks},
  author={Zhang, Ziwei and Cui, Peng and Pei, Jian and Wang, Xiao and Zhu, Wenwu},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}

[6]

@inproceedings{du2018dynamic,
  title={Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding.},
  author={Du, Lun and Wang, Yun and Song, Guojie and Lu, Zhicong and Wang, Junshan},
  booktitle={IJCAI},
  volume={2018},
  pages={2086--2092},
  year={2018}
}

[7]

@inproceedings{zuo2018embedding,
  title={Embedding temporal network via neighborhood formation},
  author={Zuo, Yuan and Liu, Guannan and Lin, Hao and Guo, Jia and Hu, Xiaoqian and Wu, Junjie},
  booktitle={Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery \& data mining},
  pages={2857--2866},
  year={2018}
}

[8]

@inproceedings{lu2019temporal,
  title={Temporal network embedding with micro-and macro-dynamics},
  author={Lu, Yuanfu and Wang, Xiao and Shi, Chuan and Yu, Philip S and Ye, Yanfang},
  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
  pages={469--478},
  year={2019}
}

[9]

@inproceedings{pareja2020evolvegcn,
  title={Evolvegcn: Evolving graph convolutional networks for dynamic graphs},
  author={Pareja, Aldo and Domeniconi, Giacomo and Chen, Jie and Ma, Tengfei and Suzumura, Toyotaro and Kanezashi, Hiroki and Kaler, Tim and Schardl, Tao and Leiserson, Charles},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={5363--5370},
  year={2020}
}

[10]

@article{rossi2020temporal,
  title={Temporal graph networks for deep learning on dynamic graphs},
  author={Rossi, Emanuele and Chamberlain, Ben and Frasca, Fabrizio and Eynard, Davide and Monti, Federico and Bronstein, Michael},
  journal={arXiv preprint arXiv:2006.10637},
  year={2020}
}

[11]

@inproceedings{xu2019spatio,
  title={Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs.},
  author={Xu, Dongkuan and Cheng, Wei and Luo, Dongsheng and Liu, Xiao and Zhang, Xiang},
  booktitle={IJCAI},
  pages={3947--3953},
  year={2019}
}

[12]

@article{goyal2018dyngem,
  title={Dyngem: Deep embedding method for dynamic graphs},
  author={Goyal, Palash and Kamra, Nitin and He, Xinran and Liu, Yan},
  journal={arXiv preprint arXiv:1805.11273},
  year={2018}
}

[13]

	@article{venkatakrishnan2018graph2seq,
  title={Graph2seq: Scalable learning dynamics for graphs},
  author={Venkatakrishnan, Shaileshh Bojja and Alizadeh, Mohammad and Viswanath, Pramod},
  journal={arXiv preprint arXiv:1802.04948},
  year={2018}
}

[14]

@article{wang2020apan,
  title={APAN: Asynchronous Propagate Attention Network for Real-time Temporal Graph Embedding},
  author={Wang, Xuhong and Lyu, Ding and Li, Mengjian and Xia, Yang and Yang, Qi and Wang, Xinwen and Wang, Xinguang and Cui, Ping and Yang, Yupu and Sun, Bowen and others},
  journal={arXiv preprint arXiv:2011.11545} Add to Citavi project by ArXiv ID,
  year={2020}
}