2026年智能感知與自主控制國際學術會議(IPAC 2026) 【重要信息】 會議官網(wǎng):https://www.yanfajia.com/action/p/NQVDXW26 會議日期: 2026年4月24-26日 會議地點:中國 · 佛山 接受或拒絕通知日期:提交后7個工作日 會議秘書:張老師 微信/電話:14748150307 郵箱:eioahv
第五屆教育創(chuàng)新與多媒體技術國際學術會議(EIMT 2026)將于2026年3月27-29日在中國蘭州召開。 Scopus 期刊征稿 (JA) 期刊將通過會議征集并評判符合發(fā)表標準的文章,符合的文章將發(fā)表至對應期刊 期刊名稱:《國際評估與教育研究雜志》 International Journal of Evaluation and Research
【IEEE出版,ICSGGE 2025 會后不到4個月EI檢索 | 中國工程院院士線下報告指導】 第五屆智能電網(wǎng)和綠色能源國際學術會議 (ICSGGE 2026) 會議時間地點:2026年3月20-22日 | 中國·海南省·東方市 大會官網(wǎng):www.icsgge.org【詳情】 主辦單位:IEEE、IEEE Power & Energy Soc
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Community is the implicit structure in social networks. In academic social networks, the users with similar or same research interests are more likely to be in the same community with close links and similar attributes. Effective community detection results can be further utilized for user analytics and user recommendation.
Anomaly detection on attributed networks is an important task in social network analysis. The goal is to find the anomalies that deviate significantly from the majority of the network in terms of some proximities, e.g. topological structure or attribute proximity. An effective anomaly detection can support many applications such as web spam detection, system fraud detection, network intrusion detection and representation learning.
Most of the existing recommendation methods assume that all the items are provided by separate producers, which is however not true in some recommendation tasks. That is, it is possible that some of the items are generated by users. Appropriately considering the user-item generation relation may bring benefit to some recommender systems, e.g., implicit recommender systems with only implicit user-item interactions.
The SCHOLAT Multiplex Network provides a comprehensive list of social information. In this network, we construct a multiplex structure with three layers: (1) The first layer represents connections between users who become friends. (2) The second layer represents connections between users who join the same groups. (3) The third layer represents connections between users who study the same courses. Furthermore, we define an individual ground-truth community based on the affiliation of users. All layers consist of the same 2,302 nodes with the highest quality. Each layer has a specific number of edges: 11,393 for the first layer, 139,004 for the second layer, and 70,226 for the third layer. We have divided these nodes into 11 communities.
開放數(shù)據(jù) - 通過SCHOLAT數(shù)據(jù)進一步推動你的研究