The DOI matches its Crossref record; 32 of the 36 references it cites were found in a registry or an index, and 4 are not checked yet.
ArnetMiner: extraction and mining of academic social networks
Abstract
This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.
Checked 2026-11-10 · 4 references not checked yet
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Citations
Cites
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
Cortes, C., & Vapnik, V. N. (1995) · Machine Learning · doi:10.1023/a:1022627411411 · Cited by 34,565
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
Lafferty, J., McCallum, A., & Pereira, F. C. N. (2001) · ScholarlyCommons (University of Pennsylvania) · Cited by 12,935
Baeza-Yates, R. A., & Ribeiro-Neto, B. A. (1999) · Cited by 11,505
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998) · IEEE Intelligent Systems and their Applications · doi:10.1109/5254.708428 · Cited by 7,243
Probabilistic Latent Semantic Indexing
Hofmann, T. (2017) · ACM SIGIR Forum · doi:10.1145/3130348.3130370 · Cited by 3,988
Probabilistic latent semantic indexing
Hofmann, T. (1999) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/312624.312649 · Cited by 3,975
An introduction to MCMC for machine learning.
Andrieu, C., de Freitas, N., Doucet, A., & Jordan, M. I. (2003) · Machine Learning · doi:10.1023/a:1020281327116 · Cited by 2,451
Supervised Topic ModelsPreprint
Blei, D., & McAuliffe, J. D. (2007) · Neural Information Processing Systems · doi:10.48550/arxiv.1003.0783 · Cited by 1,860
Flagged first, then by citations
Research works printed but not linked: 2
- N. Craswell , A. P. de Vries , and I. Soboroff . Overview of the trec-2005 enterprise track . In TREC'05 , pages 199 -- 205 , 2005 . N. Craswell, A. P. de Vries, and I. Soboroff. Overview of the trec-2005 enterprise track. In TREC'05, pages 199--205, 2005.
- A. McCallum . Multi-label text classification with a mixture model trained by em . In Proc. of AAAI'99 Workshop , 1999 . A. McCallum. Multi-label text classification with a mixture model trained by em. In Proc. of AAAI'99 Workshop, 1999.
Cited by
Sorted by citations
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
LINE: Large-scale Information Network Embedding
Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., & Mei, Q. (2015) · Proceedings of the 24th International Conference on World Wide Web · doi:10.1145/2736277.2741093 · Cited by 5,416
Self-citationDong, Y., Chawla, N. V., & Swami, A. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098036 · Cited by 2,295
Self-supervised Learning: Generative or Contrastive
Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3090866 · Cited by 2,008
Self-citation
Metadata
- Authors
- Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z.
- Affiliations
- Tsinghua University (CN)
- IBM Research (China) (CN)
- Venue
- Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2008-08-24
- Pages
- 990-998
- OpenAlex
- W2022322548
- Semantic Scholar
- f020b61789112fe7241b871907268f0bdc5c84fa
- Cited by
- 2,231
- References
- 33
- Citations by year
- 2026: 126 · 2025: 147 · 2024: 146 · 2023: 166 · 2022: 148 · 2021: 207 · 2020: 211 · 2019: 192 · 2018: 164 · 2017: 131 · 2016: 122 · 2015: 108 · 2014: 113 · 2013: 81 · 2012: 63
- FWCI
- 203.88
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Topics
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Physical Sciences › Computer Science › Information Systems › Web visibility and informetrics
- Keywords
- academic social networks, digital libraries, name ambiguity, network modeling, author co-citation networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A second model labelled each claim on 2026-11-10 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
