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paper codeĭiscriminative Deep RandomWalk for Network Classification. Max-Margin DeepWalk: Discriminative Learning of Network Representation.Ĭunchao Tu, Weicheng Zhang, Zhiyuan Liu, Maosong Sun. Revisiting Semi-supervised Learning with Graph Embeddings. paper codeĭeep Neural Networks for Learning Graph Representations. Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Me. LINE: Large-scale Information Network Embedding. GraRep: Learning Graph Representations with Global Structural Information. Mingdong Ou, Peng Cui, Fei Wang, Jun Wang, Wenwu Zhu. Non-transitive Hashing with Latent Similarity Componets. paperĭeepWalk: Online Learning of Social Representations.īryan Perozzi, Rami Al-Rfou, Steven Skiena. Keith Levin, Farbod Roosta-Khorasani, Michael W. Out-of-sample extension of graph adjacency spectral embedding. Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy. MILE: A Multi-Level Framework for Scalable Graph Embedding. Li Gao, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, Yue Hu. paperĪctive Discriminative Network Representation Learning. paperĭeep Inductive Network Representation Learning. Qixiang Wang, Shanfeng Wang, Maoguo Gong, Yue Wu. paperįeature Hashing for Network Representation Learning. Xiaobo Shen, Shirui Pan, Weiwei Liu, Yew-Soon Ong, Quan-Sen Sun. Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang.
paperĪdversarially Regularized Graph Autoencoder for Graph Embedding. Stochastic Training of Graph Convolutional Networks with Variance Reduction. Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, Yoshua Bengio. paperįastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. paperĭeep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking.Īleksandar Bojchevski, Stephan Günnemann.
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