Research and Implementation of User Features Mining Model without Labels in Social Networks
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- 발행기관
- 보안공학연구지원센터(IJUNESST) 바로가기
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- 간행물
- International Journal of u- and e- Service, Science and Technology 바로가기
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- 통권
- Vol.8 No.1 (2015.01)바로가기
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- 페이지
- pp.143-154
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- 저자
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- 언어
- 영어(ENG)
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- URL
- https://www.earticle.net/Article/A239480
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
- 영어
- Under the tide of information technology revolution, social network services (SNS) become a typical application in Web2.0 era by its rich and interactive user participation, and has swept the world in a short time. More and more users begin to express themselves on Facebook, Micro-blog and other social networks, and user features are existing in the SNS in a more intuitive way. These information assign complete personality and image for each node in the SNS, which has enormous potential commercial value. For background, this paper studies the user features mining problem in social networks, and focuses on the user features without labels. Firstly, two models are established for user features mining without labels, in which clustering, classification, text mining and graph mining are used in the model. Then, the proposed models are implemented under two scenarios: user interest discovery and Micro-group structure discovery. Experiment results based on Sina Micro-blog show that the accuracy is above 80%, which can meet the demand of user features mining in social networks. Therefore, the techniques proposed in this paper are feasible.
목차
Abstract
1. Background of Social Networks
2. Introduction of Data Mining Technology
2.1. Rise and Overview
2.2. Application of Data Mining in Social Networks
2.3. User Features Mining in Social Networks
3. User Features Mining Model without Labels
3.1. Problem Description
3.2. Clustering Model Establishment
3.3. Classification Model Establishment
4. Modeling User Interests Discovery
4.1. Data Preprocessing and Feature Weight Selection
4.2. Connection Weight Calculation
4.3. Clustering Algorithm
4.4. Model Implementation and Results Analysis
5. Modeling Micro-group Structure Discovery
5.1. Labels Set Construction
5.2. Feature Selection and Extraction
5.3. Micro-group Structure Discovery Algorithm
5.4. Model Implementation and Results Analysis
6. Conclusions
Acknowledgement
References
키워드
social networks data mining clustering classification user features저자
간행물 정보
발행기관
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- 발행기관명
- 보안공학연구지원센터(IJUNESST) [Science & Engineering Research Support Center, Republic of Korea(IJUNESST)]
- 설립연도
- 2006
- 분야
- 공학>컴퓨터학
- 소개
- 1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업
간행물
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- 간행물명
- International Journal of u- and e- Service, Science and Technology
- 간기
- 격월간
- pISSN
- 2005-4246
- 수록기간
- 2008~2016
- 십진분류
- KDC 505 DDC 605
이 권호 내 다른 논문 / International Journal of u- and e- Service, Science and Technology Vol.8 No.1
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