ArnetMiner: extraction and mining of academic social networks

By Jie Tang, Jing Zhang, Limin Yao, Juan-Zi Li, Li Zhang, Zhong Su. 2008

2,565 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Natural Language Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Philip S. Yu
2. Jie Tang
3. Zhiyuan Liu
4. Shirui Pan
5. Xiangnan He
6. Charu C. Aggarwal
7. Chengqi Zhang
8. Jure Leskovec

In Information Systems

1. Xiangnan He
2. Tat-seng Chua
3. Charu C. Aggarwal
4. Philip S. Yu
5. Jiawei Han
6. Xiang Wang
7. George Karypis
8. Jamie Callan

In Natural Language Processing

1. Chenyan Xiong
2. Jamie Callan
3. Xiang Ren
4. Mohit Bansal
5. Percy Liang
6. Daniel S. Weld
7. Jie Tang
8. William W. Cohen

Citations in the corpus

• LINE: Large-scale Information Network Embedding (2015, methodology): “We use the DBLP data set [19](3) to construct the citation networks between authors and between papers.”
• S2ORC: The Semantic Scholar Open Research Corpus (2020, background): “Citation graphs like AMiner’s Open Academic Graph (Tang et al., 2008), the Microsoft Academic Graph (MAG) (Shen et al., 2018), and the Semantic Scholar literature graph (Ammar et al., 2018), have had widespread application in bibliomet-rics, science-of-science, information retrieval, and network ana”
• A Survey on Vision--Language--Action Models for Embodied AI (2024, neutral): “Versions: [197], [198] Benchmarks: ALFRED, RoPOR [199] VirtualHome [200] 7”
• Cross-domain collaboration recommendation (2012, methodology): “org [31], an academic search system, which contains 1,436,990 authors and”
• Network Representation Learning: A Survey (2017, background): “Citation Network DBLP (PaperCitation) [1], [72] directed, binary 781,109 4,191,677 7 No Yes”
• GORC: A large contextual citation graph of academic papers (2019, background): “…graphs (e.g. the Semantic Scholar literature graph (Ammar et al., 2018), the Microsoft Academic Graph (Shen et al., 2018), AMiner’s Open Academic Graph (Tang et al., 2008), the Web of Science, 2 and others) describe connections between academic entities such as papers, authors, and institutions.”
• GraRep: Learning Graph Representations with Global Structural Information (2015, methodology): “Following [26], we totally select 6 different popular conferences and assign them into 3 groups, where WWW and KDD are grouped as data mining , NIPS and ICML as machine learning , and CVPR and ICCV as computer vision .”
• Universal Option Models (2014, methodology): “We experimented with a collection from DBLP that has about 1.5 million articles, 1 million authors, and 2 millions citations [Tang et al., 2008].”
• LaMP: When Large Language Models Meet Personalization (2023, methodology): “To generate data samples, we leverage the Citation Network Dataset (V14) (Tang et al., 2008), which comprises information on scientific papers, authors, and citations.”
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning (2021, methodology): “The datasets include two social network datasets (BlogCatalog and Flickr) and five citation network datasets (Cora, Citeseer, Pubmed, ACM and ogbn-arxiv) [61], [2], [62].”
• Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs (2015, methodology): “We deploy our algorithm and run the experiments on AMiner 1 , an online academic search system [ Tang et al. , 2008 ] .”
• A Survey on Context Learning (2017, neutral): “In [42], they furthermore incor-”
• Dynamic Network Embedding by Modeling Triadic Closure Process (2018, methodology): “We derive a co-authorship network from the academic network of AMiner4 (Tang et al. 2008).”