sreekarcheg / Citation-Count-Prediction

We survey and compare different approaches that have been suggested in the past to solve the problem of predicting the future citation count of a scientific article after a given time interval of its publication. Further, we present a novel sequence-to-sequence learning model that outperforms current state-of-art.

Future Citation Count Prediction

We survey and compare different approaches that have been suggested in the past to solve the problem of predicting the future citation count of a scientific article after a given time interval of its publication. Further, we present a comparative evaluation of the popular h-index and p-rank(a PageRank inspired model for assessing author impact) metrics.

Getting Started

DATASET

We adopted the popular Aminer dataset. The citation data is extracted from DBLP, ACM, and other sources. Each paper is associated with abstract, authors, year, venue, and title.

Prerequisites

cd src

Download the dataset

$./download_dataset.sh

##Pre-process the dataset

$./pre-process.sh

Train the model

$python feats2seq.py $python SAS.py

##RESULTS
Check Final_Report for results

Built With

• Keras

• seq2seq - sequence to sequence learning add-on for the python deep learning library Keras

Authors

Sree Ram Sreekar [e-mail]
Akshita Mittel [e-mail]
Surya Teja Chavali [e-mail]

License

This project is licensed under the MIT License - see the LICENSE.md file for details