$$\newcommand{\floor}[1]{\left\lfloor #1 \right\rfloor} \newcommand{\ceil}[1]{\left\lceil #1 \right\rceil} \newcommand{\N}{\mathbb{N}} \newcommand{\R}{\mathbb{R}} \newcommand{\Z}{\mathbb{Z}} \newcommand{\Q}{\mathbb{Q}} \newcommand{\C}{\mathbb{C}} \renewcommand{\L}{\mathcal{L}} \newcommand{\x}{\times} \newcommand{\contra}{\scalebox{1.5}{\lightning}} \newcommand{\inner}[2]{\left\langle #1 , #2 \right\rangle} \newcommand{\st}{\text{ such that }} \newcommand{\for}{\text{ for }} \newcommand{\Setcond}[2]{ \left\{\, #1 \mid #2 \, \right\}} \newcommand{\setcond}[2]{\Setcond{#1}{#2}} \newcommand{\seq}[1]{ \left\langle #1 \right\rangle} \newcommand{\Set}[1]{ \left\{ #1 \right\}} \newcommand{\set}[1]{ \set{#1} } \newcommand{\sgn}{\text{sign}} \newcommand{\halfline}{\vspace{0.5em}} \newcommand{\diag}{\text{diag}} \newcommand{\legn}[2]{\left(\frac{#1}{#2}\right)} \newcommand{\ord}{\text{ord}} \newcommand{\di}{\mathrel{|}} \newcommand{\gen}[1] \newcommand{\irr}{\mathrm{irr }} \renewcommand{\deg}{\mathrm{deg }} \newcommand{\nsgeq}{\trianglelefteq} \newcommand{\nsg}{\triangleleft} \newcommand{\argmin}{\mathrm{argmin}} \newcommand{\argmax}{\mathrm{argmax}} \newcommand{\minimize}{\mathrm{minimize}} \newcommand{\maximize}{\mathrm{maximize}} \newcommand{\subto}{\mathrm{subject\ to}} \newcommand{\DKL}[2]{D_{\mathrm{KL}}\left(#1 \di\di #2\right)} \newcommand{\ReLU}{\mathrm{ReLU}} \newcommand{\E}{\mathsf{E}} \newcommand{\V}{\mathsf{Var}} \newcommand{\Corr}{\mathsf{Corr}} \newcommand{\Cov}{\mathsf{Cov}} \newcommand{\covariance}[1]{\Cov\left(#1\right)} \newcommand{\variance}[1]{\V\left[#1\right]} \newcommand{\variancewith}[1]{\V\left[#1\right]} \newcommand{\expect}[1]{\E\left[#1\right]} \newcommand{\expectwith}[2]{\E_{#1}\left[#2\right]} \renewcommand{\P}{\mathsf{P}} \newcommand{\uniform}[2]{\mathrm{Uniform}\left(#1 \dots #2\right)} \newcommand{\gdist}[2]{\mathcal{N}\left(#1, #2\right)} \DeclarePairedDelimiter{\norm}{\lVert}{\rVert}$$ \everymath{\displaystyle}

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## What is This?

๋๋ฆ CS Research๋ฅผ ๊ฟ๊พธ๋๋ฐ๋ ์ ํ ๊ทธ๋ฐ์ชฝ์ผ๋ก๋ ์ค๋น๊ฐ ์ ๋๊ฑฐ ๊ฐ์์, ์ง๊ธ๋ถํฐ๋ผ๋ ๋ผ๋ฌธ์ฝ๊ธฐ๋ ์ธ๋ฏธ๋ ์ฐธ์ํ๊ณ  ์ ๋ฆฌํ๊ธฐ๋ฅผ ์กฐ๊ธ์ฉ ํด ๋ณด๋ ค๊ณ  ํฉ๋๋ค. ์ฌ๋ฐ์ด ๋ณด์ด๋ ๊ฒ๋ค / ์ถ์ฒ๋ฐ์ ๊ฒ๋ค ๋ฑ๋ฑโฆ ์ ์ฝ์ด๋ณด๊ณ  ์ฌ๋ฐ๋ ์ฃผ์ ๋ค์ด ์์ผ๋ฉด ์ฌ๊ธฐ์ ์ ๋ฆฌํ  ๊ณํ์๋๋ค. ๊ฐ์ธ์ ์ธ ํฅ๋ฏธ๊ฐ ์ต์ฐ์ ์ด๋ค๋ณด๋ ๋ง ์ธ์ธํ ๊ฒฐ๊ณผ๊ฐ์๊ฒ๋ณด๋จโฆ ํต์ฌ์ ์ธ ์์ด๋์ด๊ฐ ์ฌ๋ฐ๋๊ฐ? ๊ฐ ๊ฐ์ฅ ์ค์ํ๊ฒ ๊ฐ์ต๋๋ค.

์ ๊ฐ ์ด ์นดํ๊ณ ๋ฆฌ์ ๊ฒ์๊ธ์ ์์ฑํ๋ ๊ธฐ๋ณธ ํ์ ๋ค์๊ณผ ๊ฐ์ต๋๋ค.

1. Introduction : ์๊ฐ, ์ด ๋ผ๋ฌธ์ ์ฝ๊ฒ๋ ๊ณ๊ธฐ, Historically ์ด๋ค ์์น์ ์๋์ง.
2. Key ideas : ๋ณธ๋ฌธ์ Key idea๋ฅผ ์ ๋นํ ์ ๊ฐ ์ดํดํ ๋ฐฉ์๋๋ก ์ ๋ฆฌํฉ๋๋ค.
3. Conclusion : ๋ผ๋ฌธ์ ๊ฒฐ๋ก
4. Thoughts : ์ฝ์ผ๋ฉด์ ๋ค์๋ ์งง์ ์๊ฐ๋ค์ ์ ๋ฆฌํฉ๋๋ค. ์งง์ ์๊ฐ๋ค์ ์ ๋ง ๋๋์ผ์๋ ์๊ณ , ๋ญ๊ฐ ์ค ์ด๋ฐ๊ฑด ์ ์ ๋ค๋ฃจ์ง? ํ๋ ๊ฑธ์๋ ์์ํ๋ฐ ํ๋ถ์์ ๋ฒ์น ์ ๋ฐ๋ผ, ์ง๊ธ์ ์ ๊ฐ ๋ผ๋ฌธ์ ์ฝ๊ณ  ๋ญ๊ฐ ๋ ์ค๋ฅธ๊ฒ ์๋ค๋ฉด 100% ๋ ์ค ํ๋์๋๋ค. ๋๊ตฐ๊ฐ ์ด๋ฏธ ํด ๋จ๊ฑฐ๋, ์ ๋๋ ๊ฑฐ๊ฑฐ๋โฆ ํ์ง๋ง ์ฌ์ ํ ์ด๋ฐ ์๊ฐ์ ํด๋ณด๋ ๊ฒ๋ค์ ์๋ฏธ์์ง ์์๊น ์ถ์ต๋๋ค. ๊ถ๊ธํ๋ ์ ์ ๋ค๋ฅธ ๋ผ๋ฌธ์ ์ฐพ์๋ณด๋ ๊ณ๊ธฐ๋ก ์ผ์ผ๋ ค๊ณ  ํฉ๋๋ค.

๊ฒฝ์ฐ์ ๋ฐ๋ผ, ์ด๋ค ๋ผ๋ฌธ A์ ๊ทธ ํ์์ฐ๊ตฌ B, C, D๋ฅผ ํ๋ฒ์ ๋ค๋ฃจ๊ธฐ๋ ํ  ์์ ์๋๋ค.

Topic (๋ถ์ผ) ๋, ์ผ๋ฐ์ ์ผ๋ก๋ Arxiv์ ๊ธฐ์ค์ ๋ฐ๋ฆ๋๋ค. Arxiv์ ์ฌ๋ผ์์์ง ์์ ๋ผ๋ฌธ์ ์ฝ์ํ์ ์ ๊ฐ ์ต๋ํ ๋น์ทํ๊ฒ ๋ถ๋ฅํด ๋ฃ์์ต๋๋ค. (์๋ง๋) Arxiv ๋ถ๋ฅ ์ฝ๋ ์ค ์ ๊ฐ ๋ณด๊ฒ๋  ๋ผ๋ฌธ๋ค์ ์ด์ ๋๊ฐ ๋ฉ์ธ์ผ๋ฏ ํฉ๋๋ค. ํนํ ์ฅ๊ธฐ์ ์ผ๋ก๋ CC, DM, DS.

• AI : Artificial Intelligence
• CC : Computational Complexity
• DM : Discrete Mathematics
• DS : Data structures / Algortihms
• NA : Numerical Analysis

๊ทธ๋ํ์ ๊ดํ ๋ง์ ๋ผ๋ฌธ๋ค (์ ๋ DM์ด๋ DS์ฒ๋ผ ๋ฐ์๋ค์ด๊ฒ ๋๋) ์ ์ค์ ๋ก๋ ๋ค์๊ณผ ๊ฐ์ Topic์ผ๋ก ๋ง์ด ์ฌ๋ผ์ต๋๋ค. ์ ๊ฐ (์๋ง๋) DB management์ ๊ดํ ๋ญ๊ฐ๋ฅผ ์ฝ์ ์ผ์ ์์ ๊ฒ์ด๋ฏ๋ก, ์ฌ๊ธฐ ๋ด์ฉ๋ค์ ๊ฑฐ์ 100% ๊ทธ๋ํ์ ๊ดํ ๋ด์ฉ์๋๋ค.

• DB : Databases
• SI : Social and Information Networks

๋น๋ถ๊ฐ์ ์๋ฆฌ๊ณผํ๋ถ ์กธ์๋ผ๋ฌธ์ ์ํด CV, AI ์ชฝ๋ ๋ง์ด ๋ณด๊ฒ ๋  ์์ ์๋๋ค.

• CV : Computer Vision

์ด๋ค ํ ์ฃผ์ ๊ฐ ์๊ณ , ์ด ์ฃผ์ ์ ๊ดํ ์ฌ๋ฌ ๋ผ๋ฌธ์ด ์๋ ๊ฒฝ์ฐ, ๊ทธ ์ฃผ์ ์ ๋ํ ๊ฐ์๋ฅผ ์ ๋ ํฌ์คํ์ ํ๋์ฉ ๋ ์ฐ๊ธฐ๋ ํ  ๊ฒ ๊ฐ์ต๋๋ค. ์ด ํฌ์คํ์ ์๋ ๋ฆฌ๋ทฐ ๊ธ์์ ๋ผ๋ฌธ ๋ฆฌ๋ทฐ ์ธ์ ์ผ๋ฐ์ ์ธ ๊ฐ๋์ ๋ํ ์๊ฐ๋ฅผ ์ต์ํํ๊ธฐ ์ํด ์ฃผ๋ก ์์ฑํฉ๋๋ค.

Title (Link to post) Topic Published
Active Contours Without Edges NA, CV IEEE TIP1, 2001
DELTACON: A Principled Massive-Graph Similarity Function SI SDM2, 2013
In-Memory Subgraph Matching: An In-depth Study3 DS, DB SIGMOD4, 2020
Versatile Equivalences : Speeding up Subgraph Query Processing and Subgraph Matching DS, DB SIGMOD4, 2021
All Pairs Almost Shortest Paths DS FOCS5, 1996
1. Transactions on Image Processingย โฉ

2. SIAM International Conference on Data Miningย โฉ

3. Subgraph Isomorphism ๋ฐฉ๋ฒ๋ค์ ๋น๊ตํ๊ณ , ์ด๋ค์ ๋ชจ๋ ๊ตฌํํ์ฌ ํต์ผ๋ ํ๋ ์์ํฌ ์์์ ์คํํ ๋ผ๋ฌธ์ด๋ผ์ ๋ณ๋๋ก ํฌ์คํ์ ์ ๋ฆฌํ์ง๋ ์์์ต๋๋ค.ย โฉ

4. ACM SIGMOD International Conference on Management of Dataย โฉย โฉ2

5. Annual Symposium on Foundations of Computer Scienceย โฉ