郝飛,博士,歐盟瑪麗居里學者,教授,博士生/碩士生導師,國際融合科學與技術學會(IACST)中國區(qū)域主任,山西省專家學者協會信息分會常務理事,山西省區(qū)塊鏈研究會常務理事,中國計算機學會高級會員,普適計算,協同計算專業(yè)委員會委員,中國人工智能學會粒計算與知識發(fā)現、人工智能邏輯專業(yè)委員會委員,ACM會員,韓國情報處理學會會員。受韓國政府全球獎學金資助,先后在韓國科學技術院(KAIST),韓國順天鄉(xiāng)大學
蘇統華,博士,哈爾濱工業(yè)大學計算學部教授,博士生導師,計算學部副主任。主要研究領域包括大規(guī)模模式識別與手寫漢字識別、多模態(tài)媒體生成與GPU計算等。自然手寫體中文文本識別的開拓者,建立領域內首款手寫中文庫(HIT-MW庫),該庫為國內外200多家科研院所采用,曾獲得2個國際手寫漢字識別競賽第一名,連續(xù)4年評為全國最佳GPU教育工作者,獲華為昇騰領軍人物(MVP),擔任CANN技術指導委員會委員,擔任
胡青松(1978-),中國礦業(yè)大學教授,博士生導師,地下空間智能控制教育部工程研究中心副主任。畢業(yè)于信息與通信工程學科,長期從事目標定位與跟蹤、物聯網、無線通信、救災通信方面的研究工作。 更多信息請訪問胡青松學者網個人主頁: m.edingxi.cn/hqsong722 (最近更新:2026.3.12)
Loading...
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.
開放數據 - 通過SCHOLAT數據進一步推動你的研究