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# System prepended metadata

title: Paper Report
tags: [paper]

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# 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。






