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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.
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A Collective Approach to Scholar Name Disambiguation
Luo, D., Ma, S., Yan, Y., Hu, C., Zhang, X., & Huai, J. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3011674 · Cited by 10
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 ›
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