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<p>The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models.</p>

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Peer Collaboration in DBLP Using Graph Convolutional Network

  • 期刊论文

  • 发表日期：

    2025年1月11日

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  • Chintoo Kumar,
  • Showkat Ahmad Dar,
  • Charishma Batchu,
  • Kundan Sai Singamaneni,
  • Venkata Lakshmi Alekhya Panguluri

摘要

The increasing popularity of Graph-based neural network architectures plays a pivotal role in providing promising results in applications, viz., Friendship networks, Co-authorship networks, Product recommendations, etc. Many machine learning tasks can be addressed when dealing with graph representation, i.e., graph classification, node classification, link prediction, graph embedding, etc. The traditional rule-based methods have many drawbacks in comparison to graph representation learning, such as it requires manual feature engineering and struggles with unseen cases (generalizability), etc., in the case of rule-based methods. At the same time, graph representation learning overcomes those issues profoundly. The advent of graph-based deep neural network architectures inspired us to complete a graph classification problem to explore clusters of researchers who worked together in academic publications. We built the model by using a graph convolutional network to solve a multi-classification problem. We conducted a comparative study to check the efficacy of graph representation learning by GCNs on the DBLP dataset. A comparative study shows that the built model outperforms the baseline models.