王桀教授主持的科研项目: 主持国家社科基金西部项目:边境廊道交旅融合的富民效应及提升路径研究,25XJY032; 主持国家社科基金一般项目:边界效应转化与边境旅游失衡调控研究,18BGL143; 主研国家社科基金重点项目:边境旅游对国家安全影响及治理研究,21AJY023; 主持云南省社科基金重大项目:疫情防控常态化背景下云南旅游业转型升级研究,ZDZB202207; 主持云南省社科重点
【重要信息】 会议官网:https://www.yanfajia.com/action/p/BQAUFJT6 召开时间:2026年4月10-12日 会议地点:中国·西安 审核结果:5-7个工作日内 提交检索:EI Compendex, Scopus 【会议简介】 由西安理工大学计算机科学与工程学院主办,2026年第二届视觉、先进成像和计算机技
【见刊通知】ICCSEE 2025已见刊!(可联系会议秘书下载ICCSEE 2025会议论文集电子版~) 敬请期待ICCSEE 2026!--会议信息抢先看! 【重要信息】 会议官网:https://www.yanfajia.com/action/p/WRK4WPSC 召开时间:2026年04月17日-19日 会议地点:中国·天津 审稿结果通知周期:7个
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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.
开放数据 - 通过SCHOLAT数据进一步推动你的研究