📕 Predict a publication trend of AI journals / conferences using GNNs
Baseline paper: Structured Citation Trend Prediction Using Graph Neural Network

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• Python 3.7.x
• Pytorch 1.12.1+cu113
• Torch_geometric 2.1.0

We recommend using our Dockerfile to get started easily

## 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

We follow the architecture of baseline paper which is based on GATs and GCNs.
[Training stage]

[Prediction stage]

We use author, affiliation, the number of citation, title and abstract of paper, year as raw inputs. Please check this webpage for more information.

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:

Command examples

# CVPR
$ bash scripts/run_CVPR.sh

# ICASSP
$ bash scripts/run_ICASSP.sh

Note that the number of valid data are smaller than the values stated above due to insufficient sources(OpenAlex API, MAG dataset, etc..)

Frameworks: