Fetching the paper…
Reading the bibliography…
Network embedding aims to learn low-dimensional representations of nodes while capturing structure information of networks.
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Computation 9
1997
Earlier work this paper cites.
2014
Earlier work this paper cites.
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection. http://snap.stanford.edu/data (Jun 2014)
2014
Earlier work this paper cites.
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: Online Learning of Social Representations. In: SIGKDD. pp. 701–710 (2014)
2014
Earlier work this paper cites.
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: LINE: Large-scale Information Network Embedding. In: WWW. pp. 1067–1077 (2015)
2015
Earlier work this paper cites.
Fang, H., Wu, F., Zhao, Z., Duan, X., Zhuang, Y., Ester, M.: Community-based question answering via heterogeneous social network learning. In: AAAI. pp. 122–128 (2016)
2016
Earlier work this paper cites.
Grover, A., Leskovec, J.: Node2Vec: Scalable Feature Learning for Networks. In: SIGKDD. pp. 855–864 (2016)
2016
Earlier work this paper cites.
Dong, Y., Chawla, N.V., Swami, A.: Metapath2Vec: Scalable Representation Learning for Heterogeneous Networks. In: SIGKDD. pp. 135–144 (2017)
2017
Earlier work this paper cites.
Hamilton, W.L., Ying, R., Leskovec, J.: Inductive representation learning on large graphs. In: NIPS. pp. 1024–1034 (2017)
2017
Earlier work this paper cites.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: ICLR (2017)
2017
Earlier work this paper cites.
Tu, C., Liu, H., Liu, Z., Sun, M.: CANE: Context-aware network embedding for relation modeling. In: ACL. pp. 1722–1731 (Jul 2017)
2017
Earlier work this paper cites.
Cai, H., Zheng, V.W., Chang, K.: A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE TKDE 30
2018
Earlier work this paper cites.
Du, L., Wang, Y., Song, G., Lu, Z., Wang, J.: Dynamic Network Embedding : An Extended Approach for Skip-gram based Network Embedding. In: IJCAI. pp. 2086–2092 (2018)
2018
Earlier work this paper cites.
Gligorijević, V., Barot, M., Bonneau, R.: deepnf: Deep network fusion for protein function prediction. Bioinformatics 34
2018
Earlier work this paper cites.
2018
Cited alongside, same era.
Li, T., Zhang, J., Yu, P.S., Zhang, Y., Yan, Y.: Deep dynamic network embedding for link prediction. IEEE Access 6
2018
Cited alongside, same era.
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph Attention Networks. ICLR (2018)
2018
Cited alongside, same era.
Wen, Y., Guo, L., Chen, Z., Ma, J.: Network embedding based recommendation method in social networks. In: WWW. pp. 11–12 (2018)
2018
Cited alongside, same era.
Zhang, H., Qiu, L., Yi, L., Song, Y.: Scalable multiplex network embedding. In: IJCAI. pp. 3082–3088 (2018)
2018
Cited alongside, same era.
Milani Fard, A., Bagheri, E., Wang, K.: Relationship prediction in dynamic heterogeneous information networks. In: ECIR. pp. 19–34 (2019)
2019
Later among the works it cites.
Nelson, W., Zitnik, M., Wang, B., Leskovec, J., Goldenberg, A., Sharan, R.: To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics 10
2019
Later among the works it cites.
Sajadmanesh, S., Bazargani, S., Zhang, J., Rabiee, H.R.: Continuous-time relationship prediction in dynamic heterogeneous information networks. ACM TKDD 13
2019
Later among the works it cites.
Sankar, A., Wu, Y., Gou, L., Zhang, W., Yang, H.: Dynamic graph representation learning via self-attention networks. In: Worshop on Representation Learning on Graphs and Manifolds in ICLR (2019)
2019
Later among the works it cites.
Shi, C., Hu, B., Zhao, W.X., Yu, P.S.: Heterogeneous information network embedding for recommendation. IEEE TKDE 31
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Zhang, Z., Cui, P., Pei, J., Wang, X., Zhu, W.: Timers: Error-bounded svd restart on dynamic networks. In: AAAI (2018)
2018
Cited alongside, same era.
Zhou, L.k., Yang, Y., Ren, X., Wu, F., Zhuang, Y.: Dynamic network embedding by modeling triadic closure process. In: AAAI (2018)
2018
Cited alongside, same era.
Bian, R., Koh, Y.S., Dobbie, G., Divoli, A.: Network Embedding and Change Modeling in Dynamic Heterogeneous Networks. In: SIGIR. pp. 861–864 (2019)
2019
Cited alongside, same era.
Cen, Y., Zou, X., Zhang, J., Yang, H., Zhou, J., Tang, J.: Representation Learning for Attributed Multiplex Heterogeneous Network. In: SIGKDD. pp. 1358–68 (2019)
2019
Cited alongside, same era.
Chen, J., Zhang, J., Xu, X., Fu, C., Zhang, D., Zhang, Q., Xuan, Q.: E-lstm-d: A deep learning framework for dynamic network link prediction. IEEE Transactions on Systems, Man, and Cybernetics: Systems pp. 1–14 (2019)
2019
Cited alongside, same era.
Cui, P., Wang, X., Pei, J., Zhu, W.: A survey on network embedding. IEEE TKDE 31
2019
Cited alongside, same era.
Goyal, P., Chhetri, S.R., Canedo, A.: dyngraph2vec: Capturing network dynamics using dynamic graph representation learning. Knowledge-Based Systems (Jul 2019)
2019
Cited alongside, same era.
2019
Later among the works it cites.
Singer, U., Guy, I., Radinsky, K.: Node Embedding over Temporal Graphs. In: IJCAI. pp. 4605–4612 (2019)
2019
Later among the works it cites.
Trivedi, R., Farajtabar, M., Biswal, P., Zha, H.: Dyrep: Learning representations over dynamic graphs. In: ICLR (2019)
2019
Later among the works it cites.
Xu, D., Cheng, W., Luo, D., Liu, X., Zhang, X.: Spatio-Temporal Attentive RNN for Node Classification in Temporal Attributed Graphs. In: IJCAI (2019)
2019
Later among the works it cites.
Xue, H., Peng, J., Li, J., Shang, X.: Integrating multi-network topology via deep semi-supervised node embedding. In: CIKM. pp. 2117–2120 (2019)
2019
Later among the works it cites.
Yin, Y., Ji, L., Zhang, J., Pei, Y.: Dhne: Network representation learning method for dynamic heterogeneous networks. IEEE Access 7
2019
Later among the works it cites.
Pareja, A., Domeniconi, G., Chen, J., Ma, T., Suzumura, T., Kanezashi, H., Kaler, T., Schardl, T.B., Leiserson, C.E.: EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In: AAAI (2020)
2020
Closest in time.
Peng, J., Xue, H., Wei, Z., Tuncali, I., Hao, J., Shang, X.: Integrating multi-network topology for gene function prediction using deep neural networks. Briefings in Bioinformatics (2020)
2020
Closest in time.
Yang, L., Xiao, Z., Jiang, W., Wei, Y., Hu, Y., Wang, H.: Dynamic heterogeneous graph embedding using hierarchical attentions. In: European Conference on Information Retrieval (2020)
2020
Closest in time.