graph-dive

숨참고 GRAPH-DIVE

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📕 논문의 트렌드를 예측하는 그래프 모델

Baseline paper: Structured Citation Trend Prediction Using Graph Neural Network

내 논문의 주제는 citation을 많이 받는 ♨️핫한 주제일까❓

1.Requirements

Verisions (Recommended)

Python 3.7.15
Pytorch 1.12.1+cu113
Torch_geometric 2.1.0

Docker

Dockerfile 제공

# build docker image
$ docker build -t graph-dive:latest . 

# execute docker container
$ docker run --name graph-dive --ipc=host -it -v <working_dir>:/workspace -w /workspace graph-dive:latest /bin/bash

2.Model

3.Dataset

MAG(Microsoft Academic Graph)

[Fig 1. MAG Scheme]
논문 제목, 저자의 명성, Abstract, citation 수, conference id를 필터링해서 사용.

Data directory tree

Directory tree including data should be as follows:

├─graph-dive/
└─data/
	├─ affiliationembedding.csv
	├─ edge_data/
	│   ├─ 1158167855_refs.csv #{CVPR_conference_id}_refs.csv
	│   ├─ 1184914352_refs.csv #{AAAI_conference_id}_refs.csv
	│   └─ ...
	├─ year_data/
	│   ├─ 1158167855.csv #{CVPR_conference_id}.csv
	│   ├─ 1184914352.csv #{AAAI_conference_id}.csv 
	│   └─ ...
	├─ json_1158167855/ # CVPR
	│   ├─ {paper_id1}.json
	│   ├─ {paper_id2}.json
	│   └─ ...
	├─ json_1184914352/ # AAAI
	│   └─ ...
	...

For each journal/conference, conference IDs are look like:

Conference	Conference ID	# of data
ICML	1180662882	8653
ICASSP	1121227772	16997
NeurIPS	1127325140	8113
AAAI	1184914352	13766
EMNLP	1192655580	5667
CVPR	1158167855	13058
ICDM	1183478919	4169
CIKM	1194094125	4201

4.Evaluation

📝 SKILLS

Frameworks: