mlsanityData to 14 Oct 2026

GraphConfRec: A Graph Neural Network-Based Conference Recommender System

arXiv 2106.12340 ↗First posted 23 Jun 2021cs.IR cs.DL cs.LGcited by 2

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Abstract (arXiv)

In today's academic publishing model, especially in Computer Science, conferences commonly constitute the main platforms for releasing the latest peer-reviewed advancements in their respective fields. However, choosing a suitable academic venue for publishing one's research can represent a challenging task considering the plethora of available conferences, particularly for those at the start of their academic careers, or for those seeking to publish outside of their usual domain. In this paper, we propose GraphConfRec, a conference recommender system which combines SciGraph and graph neural networks, to infer suggestions based not only on title and abstract, but also on co-authorship and citation relationships. GraphConfRec achieves a recall@10 of up to 0.580 and a MAP of up to 0.336 with a graph attention network-based recommendation model. A user study with 25 subjects supports the positive results.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 15 of its 43 references that are in the map. First posted on arXiv 23 Jun 2021.

Builds on

15 of 43 references.

References · 15

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3 more
  1. 2013A Semantic Matching Energy Function for Learning with Multi-relational Datacited by 61
  2. 2019A Context-Aware Citation Recommendation Model with BERT and Graph Convolutional Networkscited by 29
  3. 2019Advancing GraphSAGE with A Data-Driven Node Samplingcited by 6

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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 1.

2 papers.

  1. 2022Artificial intelligence technologies to support research assessment: A reviewcited by 1
  2. 2024Assistive AI for Augmenting Human Decision-makingcited by 0