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