PPT-Neighborhood Based Fast Graph Search In Large Networks

Arijit Khan Nan Li Xifeng Yan Ziyu Guan Computer Science UC Santa Barbara arijitkhan nanli xyan ziyuguan csucsbedu Supriyo Chakraborty UC Los Angeles. Introduction. 2. Social . networks model social relationships by . graph structures. using vertices and edges. . Vertices. model individual social actors in a network, . while . edges. model relationships between social actors.. Homotopy. Class Constraints. Subhrajit Bhattacharya . Vijay Kumar. Maxim . Likhachev. University of. Pennsylvania. GRASP. L. ABORATORY. Addendum. For the simple cases in 2-dimensions we have not distinguished between . Isabelle Stanton, UC Berkeley. Gabriel . Kliot. , Microsoft Research XCG. Modern graph datasets are huge. The web graph had over a trillion links in 2011. Now?. . facebook. has “more than 901 million users with average degree 130”. Building Search-Driven Portals . with Microsoft Office SharePoint Server 2007 and Microsoft Silverlight .  Jan Helge Sageflåt. . Director, Product Development. . FAST, A Microsoft® Subsidiary. Borislav. . Nikoli. ć. , . Hazem. Ismail Abdel Aziz Ali, . Kostiantyn. . Berezovskyi. , Ricardo . Garibay. Martinez, Muhammad Ali . Awan. The Outline. Introduction. Heuristics. Local search. Metaheuristics. Jing . Zhang, . Jie. . Tang . , Cong . Ma . , . Hanghang. . Tong . , Yu . Jing . , and . Juanzi. . Li. Presented by Moumita Chanda Das . Outline. Introduction. Problem formulation. Panther using path sampling. a Multi-Layered Indexing Approach. Yongjiang Liang, . Peixiang Zhao. CS @ FSU. zhao@cs.fsu.edu. Outline. Introduction. State-of-the-art solutions. ML-Index & similarity search. Experiments. Conclusion. KDD, New York City. August 26, 2014. Manish . Purohit. ^. , . B. Aditya Prakash. *. , . Chanhyun. Kang. ^. , Yao Zhang. *. , V S . Subrahmanian. ^. . . *. Virginia Tech . ^. University of Maryland. EMC Symmetrix VMAX, FAST VP, Microsoft Hyper-V. Agenda. Solution . o. verview: Objectives. Why this solution. Solution architecture (physical and virtualized). Results and findings (physical and virtualized). Fanjin. Zhang, Xiao Liu, . Jie. Tang, . Yuxiao. Dong, . Peiran. Yao, . Jie. Zhang, . Xiaotao. Gu, Yan Wang, Bin Shao, Rui Li and . Kuansan. Wang.. Tsinghua University Microsoft Research. Arijit. Khan, Nan Li, . Xifeng. Yan, . Ziyu. Guan. Computer Science . UC Santa Barbara. {. arijitkhan. , . nanli. , . xyan. , . ziyuguan. }@. cs.ucsb.edu. . Supriyo. Chakraborty. UC Los Angeles. Lingxiao Ma. . †. , Zhi Yang. . †. , Youshan Miao. ‡. , Jilong Xue. ‡. , Ming Wu. ‡. , Lidong Zhou. ‡. , . Yafei. Dai. . †. †. . Peking University. ‡ . Microsoft Research. USENIX ATC ’19, Renton, WA, USA. William L. Hamilton, Rex Ying, Jure . Leskovec. Keshav Balasubramanian. Outline. Main goal: generating node embeddings. Survey of past methods. GCNs. GraphSAGE. Algorithm. Optimization and learning. Aggregators. Zhihao Jia. 1. 6/23/19. Stanford University. Deep Learning is Everywhere. 2. Recurrent Neural Networks. Convolutional Neural Networks. Neural Architecture Search. Reinforcement Learning. Deep Learning Deployment is Challenging.