[tile] DB libraries, and large-scale graph analytics. Open-source software projects resulting from his work include the Matrix Template Library, the Boost Graph Library and Open MPI. 。Phil Ratzloff *。Distinguished relationships between elements of a data set *，Without regard to what the data set actually is *。 Graph theoretical (abstract) results can be applied to many different practical (concrete) problems -theory cvMuNKCATIOWS 有二有 灿瑟必帮 和一是/第 手太则光大 A，训四 昱 站 -本| 厅 是，主 半一司、|全之体 ~ 对 /人-看 奋才全 和 全原 和 刁曾 The Future ls Big Graph: on meetalThngs soneaiadman MD] Basic Principles *。The C++ standard library (nee STL) provides

Selection ✓ Order by ❏ Group by ✓ Query ✓ Limit ✓ Expression Compatibility ✓ Join ✓ Subquery ✓ Graph traverse ✓ Out direction ✓ In direction ✓ Bidirection ✓ Edge traverse ✓ Distinct Index 01.

Scott, Principal Engineer at CMU SEI Graph/ML/AI algorithms for large- and small- scale parallel systems. Working on GBTL, a linear algebra-based C++ library for graph analytics.[DISTRIBUTION STATEMENT GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 4[DISTRIBUTION STATEMENT A] This material has been approved for public release GraphBLAS community, C API Overview of our draft C++ API How might this interoperate with standard C++, graph library proposal? 5[DISTRIBUTION STATEMENT A] This material has been approved for public release

Knowledge Graph meet Python Yongpan Sheng 目录 CONTENTS The Pipeline of Knowledge Graph Construction by Data- driven manner Python Tools for Graph Data Management Domain-specific Knowledge Graph Construction relation, object>  Mapping from natural questions to structured queries executable on knowledge graph （机器的潜台词：“我”会推理，so easy ！）。 所以，通俗的来说，在AI system中：要么从原有的知识体系中直接提取信息来使用，要 么进行推理。 将知识融合在机器中，使机器能够利 BigKE将显著提升机器的认知水平。 Preliminaries 本页PPT借鉴于复旦大学肖仰华老师《大数据时代的知识工程与知识管理》 Knowledge Graph – KG引领KE复兴  Knowledge graph is a large-scale semantic network consisting of entities and concepts as well as

top-down task graph What is Task Graph-based Programming System (TGPS) Code 4• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based Programming task graph What is Task Graph-based Programming System (TGPS) Code A B C D B A C D 6• TGPS encapsulates function calls and their dependencies in a top-down task graph What is Task Graph-based precede(task_d); 19 20 sched.schedule(); 21 sched.wait(); B A C D 1. Easy to write and express a task graph 2. Allow to implement irregular parallel decomposition strategies 8Existing TGPSs on Heterogenous

conflict ● Portability ○ Flag meanings depend on the compiler in use (vendor and version) ● Build graph is unknown ○ Generated headers? ○ Generated sources? ○ Generated input files (-include, -fmodule-mapper= -fmodule-mapper=, response files) ● C++ modules… Compilation Databases: Limitations 2 9C++ Modules on the Scene ● Modules complicate C++ compilation ○ Surprise! 🎉 ○ Basically inherit the Fortran 90 modules CXX_MODULES FILES a1.cpp) target_compile_features(A PUBLIC cxx_std_20) 12C++ Modules on the Scene: Compilation Strategy ● CMake uses the “scanning” approach to build Fortran and C++ modules See:

Scene Setting “… functions then provide the basic vocabulary of computation, just as types (built-in and user-defined) provide the basic vocabulary of data.” Bjarne Stroustrup, A Tour of C++, Second struct Foo { int n; std::vector bar_vec; std::string foo_str; }; Scene Setting Foo int std::vector std::string Bar std::string Bar std::string Bar std::stringObject Source Type Graph .o w/ DebugInfostruct Bar { std:string str; }; struct Foo { int n; std::vector bar_vec; std::string foo_str; }; Source Type Graph .o w/ DebugInfostruct

Chuan Zhou, and Yue Hu. 2020. Graph neural architecture search. In IJCAI, Vol. 20. 1403–1409. [12] Matheus Nunes and Gisele L Pappa. 2020. Neural Architecture Search in Graph Neural Networks. In Brazilian 317. [13] Huan Zhao, Lanning Wei, and Quanming Yao. 2020. Simplifying Architecture Search for Graph Neural Network. arXiv preprint arXiv:2008.11652 (2020). [14] Jin Xu, Mingjian Chen, Jianqiang Huang [20] Chengshuai Zhao, Yang Qiu, Shuang Zhou, Shichao Liu, Wen Zhang, and Yanqing Niu. 2020. Graph embedding ensemble methods based on the heterogeneous network for lncRNA-miRNA interaction prediction

representing the scene locations from the first two acts of Shakespeare’s Romeo and Juliet: 1 let romeoAndJuliet = [ 2 "Act 1 Scene 1: Verona, A public place", 3 "Act 1 Scene 2: Capulet's mansion" "Act 1 Scene 3: A room in Capulet's mansion", 5 "Act 1 Scene 4: A street outside Capulet's mansion", 6 "Act 1 Scene 5: The Great Hall in Capulet's mansion", 7 "Act 2 Scene 1: Outside "Act 2 Scene 2: Capulet's orchard", 9 "Act 2 Scene 3: Outside Friar Lawrence's cell", 10 "Act 2 Scene 4: A street in Verona", 11 "Act 2 Scene 5: Capulet's mansion", 12 "Act 2 Scene 6: Friar

representing the scene locations from the first two acts of Shakespeare’s Romeo and Juliet: 1 let romeoAndJuliet = [ 2 "Act 1 Scene 1: Verona, A public place", 3 "Act 1 Scene 2: Capulet's mansion" "Act 1 Scene 3: A room in Capulet's mansion", 5 "Act 1 Scene 4: A street outside Capulet's mansion", 6 "Act 1 Scene 5: The Great Hall in Capulet's mansion", 7 "Act 2 Scene 1: Outside mansion", 8 "Act 2 Scene 2: Capulet's orchard", 9 "Act 2 Scene 3: Outside Friar Lawrence's cell", 10 "Act 2 Scene 4: A street in Verona", 11 "Act 2 Scene 5: Capulet's mansion",