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클리크 마이닝에 기반한 새로운 커뮤니티 탐지 알고리즘 연구
양예선 ( Yixuan Yang ),펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),김석훈 ( Seok-hoon Kim ),이혜정 ( Hyejung Lee ),싯소포호트 ( Sophort Siet ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
Community detection is meaningful research in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper proposes a method to detect community by detecting maximal cliques and obtain the high influence cliques by high influence nodes, then merge the cliques with high similarity in social network.
서명된 속성 소셜 네트워크에서의 Absolute-Fair Maximal Balanced Cliques 탐색
양예선 ( Yixuan Yang ),펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),이혜정 ( Hyejung Lee ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
Community detection is a hot topic in social network analysis, and many existing studies use graph theory analysis methods to detect communities. This paper focuses on detecting absolute fair maximal balanced cliques in signed attributed social networks, which can satisfy ensuring the fairness of complex networks and break the bottleneck of the information cocoon .
펭소니 ( Sony Peng ),양예선 ( Yixuan Yang ),박두순 ( Doo-soon Park ),이혜정 ( Hyejung Lee ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.1
With the tremendous rise in popularity of the Internet and technological advancements, many news keeps generating every day from multiple sources. As a result, the information (News) on the network has been highly increasing. The critical problem is that the volume of articles or news content can be overloaded for the readers. Therefore, the people interested in reading news might find it difficult to decide which content they should choose. Recommendation systems have been known as filtering systems that assist people and give a list of suggestions based on their preferences. This paper studies a personalized news recommendation system to help users find the right, relevant content and suggest news that readers might be interested in. The proposed system aims to build a hybrid system that combines collaborative filtering with content-based filtering to make a system more effective and solve a cold-start problem. Twitter social media data will analyze and build a user's profile. Based on users' tweets, we can know users' interests and recommend personalized news articles that users would share on Twitter.
POI 에서 딥러닝을 이용한 개인정보 보호 추천 시스템
펭소니 ( Sony Peng ),박두순 ( Doo-soon Park ),김대영 ( Daeyoung Kim ),양예선 ( Yixuan Yang ),이혜정 ( Hyejung Lee ),싯소포호트 ( Sophort Siet ) 한국정보처리학회 2022 한국정보처리학회 학술대회논문집 Vol.29 No.2
POI refers to the point of Interest in Location-Based Social Networks (LBSNs). With the rapid development of mobile devices, GPS, and the Web (web2.0 and 3.0), LBSNs have attracted many users to share their information, physical location (real-time location), and interesting places. The tremendous demand of the user in LBSNs leads the recommendation systems (RSs) to become more widespread attention. Recommendation systems assist users in discovering interesting local attractions or facilities and help social network service (SNS) providers based on user locations. Therefore, it plays a vital role in LBSNs, namely POI recommendation system. In the machine learning model, most of the training data are stored in the centralized data storage, so information that belongs to the user will store in the centralized storage, and users may face privacy issues. Moreover, sharing the information may have safety concerns because of uploading or sharing their real-time location with others through social network media. According to the privacy concern issue, the paper proposes a recommendation model to prevent user privacy and eliminate traditional RS problems such as cold-start and data sparsity.