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
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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
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