# Paper Report ###### tags: `paper` [toc] ### Part 1. Machine Learning 1. [Few-Shot Learning with Graph Neural Networks (ICLR, 2018)](https://openreview.net/pdf?id=BJj6qGbRW) 2. [Gated Graph Sequence Neural Networks (ICLR, 2016, cite 787 )](https://arxiv.org/pdf/1511.05493.pdf) 3. :+1:[Semi-Supervised Classification with Graph Convolutional Networks (ICLR, 2017, cite 3065)](https://openreview.net/pdf?id=SJU4ayYgl) 5. [Graph Representation Learning via Hard and Channel-Wise Attention Networks (KDD, 2019, cite 4)](https://dl.acm.org/doi/pdf/10.1145/3292500.3330897) 6. [Graph Attention Networks (ICLR,2018 cite 1052)](https://openreview.net/pdf?id=rJXMpikCZ) > [color=#0000ff] > > 你們自己看看喜歡哪一篇,可以在底下留言 > 我選3 5 >[name=Conan] >[color=#aa2236] > >3我喜歡 >[name=Omnom] > ### Part 2. Circuit 1. Majority-Inverter Graph * **Unavailabe** [Majority-Inverter Graph: A Novel Data-Structure and Algorithms for Efficient Logic Optimization](https://wiki.epfl.ch/edicpublic/documents/Candidacy%20exam/Amar%20Gaillardon%20Micheli%20-%202014%20-%20Majority-Inverter%20Graph%20A%20Novel%20Data-Structure%20and%20Algorithms%20for%20Efficient%20Logic%20Optimization%20copy.pdf)<br/> (DAC,2014: cite 53) * [Majority-Inverter Graph: A New Paradigm for Logic Optimization ](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7293649)<br/> (IEEE Trans. on CAD of Integrated Circuits and Systems,2016: cite 45) 2. [Minimum Circuit Size, Graph Isomorphism, And Related Problems ](https://epubs.siam.org/doi/pdf/10.1137/17M1157970)<br/> (SIAM,2018: cite 6) 3. [Isomorphism-Aware Identification of Custom Instructions With I/O Serialization ](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6387693)<br/>(IEEE Trans. on CAD of Integrated Circuits and Systems,2013: cite 6) 4. [Rule-Level Verification of Graph Transformations for Invariants Based on Edges' Transitive Closure ](https://link.springer.com/content/pdf/10.1007%2F978-3-642-40561-7_8.pdf) <br/> (SEFM,2013: cite : 4? ) 5. [Automata for the verification of monadic second-order graph properties ](https://reader.elsevier.com/reader/sd/pii/S1570868312000560?token=3E047757E2D15F80CE068377D9499DE95572A1863F36F7C7D8667F95B8D4C00307831CAD333B1AB4D2FB8739ECCF206A) <br/> (J. Appl. Log, 2012: cite 16) 6. [Fast Isomorphism Testing for a Graph-based Analog Circuit Synthesis Framework ](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6176570)<br/> (DATE,2012: cite 5) 7. :+1:[Retina Verification System Based on Biometric Graph Matching ](https://ieeexplore.ieee.org/abstract/document/6523153) IEEE TRANSACTIONS ON IMAGE PROCESSING CITE 48 >[color=#aa2236] > >喜歡或不喜歡都可以留言 >[name=Omnom] > >[color=#aa2236] > >第二篇有點艱澀 >[name=Omnom] > >[color=#0000ff] > >第二篇先不要 >我選1-2或6 >[name=Conan] > ### RULE ![](https://i.imgur.com/AkoYv3c.jpg) ![](https://i.imgur.com/JM1QxVc.png) ### Second paper Unavailable 1. FASTrust: Feature Analysis for Third-Party IP 2. Numerical Formulation and Simulation of Social Networks Using Graph Theory on Social Cloud Platform 3. Towards Graph Based Parallel Sparse Solver for Circuit Simulation Problems 4. Majority-Inverter Graph: A novel data-structure and algorithms for efficient logic optimization 5. Deep Expander Networks: Efficient Deep Networks from Graph Theory 6. Weighted System Dependence Graph 7. Efficient Reversible Logic Synthesis via Isomorphic Subgraph Matching 8. A Structural Approach to Offline Signature Verification Using Graph Edit Distance 9. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering 10. Toward an uncertainty principle for weighted graphs ### Tool (sci-hub) ### second paper candidate ~~[Author verification using a Graph-based Representation 2015 cite=8](https://www.ijcaonline.org/research/volume123/number14/castillo-2015-ijca-905654.pdf)~~ ~~[BGG: A Graph Grammar Approach for Software Architecture Verification and Reconfiguration 2013 cite=10](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6603687)~~ :+1:[Retina Verification System Based on Biometric Graph Matching ](https://ieeexplore.ieee.org/abstract/document/6523153) IEEE TRANSACTIONS ON IMAGE PROCESSING CITE 48 ~~[Brain complex network analysis by means of resting state fMRI and graphanalysis](https://reader.elsevier.com/reader/sd/pii/S1525505013006173?token=3E69BBEFEF541E01A9EAA884CCA9D92DBC538D9190AC4EDBD8734213C583FD6B937A919696B88AA3E5DA3FB3BF832C36)~~ ### Other [Why Graph-Based Verification? ](https://brekersystems.com/wp-content/uploads/2018/04/breker-why-graphs-whitepaper.pdf) 某家做半導體verification的公司發的介紹,在比較基於graph的verification跟一般常用的UVM standard。