Swarm and Evolutionary Computation Dual-stage self-adaptive differential evolution with complementary and ensemble mutation strategies Highlights • Integrate optimization experience and
该论文发表于IEEE Journal of Biomedical and Health Informatics (中科院二区,IF=6.7),题目为《Cross-Modal Guiding Neural Network for Multimodal Emotion Recognition From EEG and Eye Movement Signals》。 青岛大学未来研究院和自动化学院的付宝
【摘要】华南理工大学生物医学科学与工程学院吴凯教授与广州医科大学附属脑科医院吴逢春主任团队合作,开展了基于脑电周期与非周期成分分离来分析重度抑郁症神经生理机制及脑电生物学标志物的研究。2025年1月,《Biological Psychiatry: Cognitive Neuroscience and Neuroimaging》杂志录用了题为《Individualized Spectral Feat
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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