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Guodong Long

Guodong Long has 3 papers in our library, in 2017–2023, most of them on Language models & pretraining, Information retrieval & search; often with Chengqi Zhang, Shirui Pan, Tao Shen.

3 papers in our library · 2017–2023

OpenAlex · ORCID · Wikidata · h-index 49 · 286 works · 15,087 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 419 times in our library · 6 more in our library without a page yet · ⭐ Paper Stars ranking: #270

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of Technology SydneySydney · Australia2011–2026 · 226 papersEmployment 2020–2026 · Asociate Professor, Faculty of Engineering and ITEmployment since 2026 · Professor, Faculty of Engineering and ITcurrent🏢MicrosoftRedmond · United States2019–2023 · 4 papers🎓The University of QueenslandBrisbane · Australia2015–2019 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 workArts and Humanities · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Information retrieval & search · 1 workLanguage models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Natural language understanding · 1 work

 Works by year

2020s

20231 paper

2010s

20191 paper20171 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Comprehensive Survey on Graph Neural Networks (2019 · 10,312 citations)
    Deep learning works well on grid-like data such as images and text sequences. Many real datasets are graphs instead: nodes have varying numbers of unordered neighbours, and instances depend on each other. This survey reviews deep learning methods for graph data and sorts them into four families. Recurrent GNNs…
  2. DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language Understanding (2017 · 854 citations)
    Sentence encoders in NLP usually rely on RNNs for long-range context or on CNNs for local patterns. Both have drawbacks: RNNs process tokens sequentially, and standard attention ignores word order. The authors propose DiSAN, a lightweight sentence-encoding network that uses only attention. It has two new components.…
  3. Large Language Models are Strong Zero-Shot Retriever (2023 · 26 citations)
    Zero-shot first-stage retrieval, where only a raw document collection is available and no labeled query–document pairs exist, usually trails supervised systems.

All works

9 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. A Comprehensive Survey on Graph Neural NetworksarXiv:1901.00596Zonghan Wu, Shirui Pan, Fengwen Chen, et al.2019 · 10,312 citations
  2. Graph WaveNet for Deep Spatial-Temporal Graph Modeling ↗International Joint Conference on Artificial Intelligence · no summary yetZonghan Wu et al.2019 · 2,968 citations
  3. DiSAN: Directional Self-Attention Network for RNN/CNN-Free Language UnderstandingProceedings of the AAAI Conference on Artificial IntelligenceTao Shen, Tianyi Zhou, Guodong Long, et al.2017 · 854 citations
  4. FedProto: Federated Prototype Learning across Heterogeneous Clients ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetYue Tan et al.2022 · 766 citations
  5. Attributed Graph Clustering: A Deep Attentional Embedding Approach ↗International Joint Conference on Artificial Intelligence · no summary yetChun Wang et al.2019 · 582 citations
  6. Multi-Task Learning for Conversational Question Answering over a Large-Scale Knowledge Base ↗Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · no summary yetTao Shen et al.2019 · 110 citations
  7. Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning ↗Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) · no summary yetTao Shen et al.2020 · 87 citations
  8. Robust Semi-Supervised Learning through Label Aggregation ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetYan Yan et al.2016 · 53 citations
  9. Large Language Models are Strong Zero-Shot RetrieverarXiv:2304.14233Tao Shen, Guodong Long, Xiubo Geng, et al.2023 · 26 citations

 Co-authors here

9 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 419 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

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