OSN Graph Embedding

Graph Embedding

• {Perozzi14} Bryan Perozzi and Rami Al-Rfou and Steven Skiena. DeepWalk: Online Learning of Social Representations. Proc. of KDD, 2014. develop an algorithm (DeepWalk) that learns social representations of a graph’s vertices, by modeling a stream of short random walks
• {Tang15} Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei. LINE: Large-scale Information Network Embedding. Proc. of WWW, 2015.
• {Tang15} Jian Tang, Meng Qu, and Qiaozhu Mei. PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks. Proc. of KDD, 2015.
• {Grover16} Aditya Grover, Jure Leskovec. node2vec: Scalable Feature Learning for Networks. Proc. of KDD, 2016.
• {Hamilton17} William L. Hamilton, Rex Ying, Jure Leskovec. Inductive Representation Learning on Large Graphs. Proc. of NIPS, 2017. GraphSAGE, a generalinductiveframework that leverages node featureinformation (e.g., text attributes) to efficiently generate node embeddings forpreviously unseen data
• {Backes17} Michael Backes, Mathias Humbert, Jun Pang, Yang Zhang. walk2friends: Inferring Social Links from Mobility Profiles. Proc. of ACM CCS, 2017. bipartite graph embedding
• {Qiu18} Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, and Jie Tang. Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. Proc. of WSDM, 2018.
• {Yu18} Yanwei Yu, Hongjian Wang, Zhenhui Li. Inferring Mobility Relationship via Graph Embedding. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2018, 2(3):147:1-147:21.
• {Qiu19} Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang. NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. Proc. of WWW, 2019.
• {Yang19} Dingqi Yang, Bingqing Qu, Jie Yang and Philippe Cudre-Mauroux. Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach. Proc. of WWW, 2019. LBSN2vec

Graph embedding (dynamic)

• {Du18} Lun Du, Yun Wang, Guojie Song, Zhicong Lu, Junshan Wang. Dynamic Network Embedding: An Extended Approach for Skip-gram based Network Embedding. Proc. of IJCAI, 2018. an extension for the Skip-gram based network embedding methods, which can keep the optimality of the objective in the Skip-gram based methods in theory; not only generalize to the new vertex representation, but also update the most affected original vertex representations during the evolvement of the network
• {Zhou18} Lekui Zhou, Yang Yang, Xiang Ren, Fei Wu, Yueting Zhuang. Dynamic Network Embedding by Modeling Triadic Closure Process. Proc. of AAAI, 2018. a novel representation learning approach, DynamicTriad, to preserve both structural informa- tion and evolution patterns of a given network; DynamicTriad can clearly separate researchers from different communities comparing with other methods
• {Kumar19} Srijan Kumar, Xikun Zhang, Jure Leskovec. Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. Proc. of KDD, 2019. JODIE, a coupled recurrent neural network model that learns the embedding trajectories of users and items

Graph embedding (GPU)

• {Zhu19} Zhaocheng Zhu, Shizhen Xu, Meng Qu, and Jian Tang. GraphVite:A High-Performance CPU-GPU Hybrid System for Node Embedding. Proc. of WWW, 2019.
• {Jangda21} Abhinav Jangda, Sandeep Polisetty, Arjun Guha, and Marco Serafini. Accelerating Graph Sampling for Graph Machine Learning using GPUs. Proc. of ACM EuroSys, 2021.