首页|Reports from Xidian University Describe Recent Advances in Computational Intelli gence (Graph Contrastive Learning for Tracking Dynamic Communities In Temporal N etworks)
Reports from Xidian University Describe Recent Advances in Computational Intelli gence (Graph Contrastive Learning for Tracking Dynamic Communities In Temporal N etworks)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - A new study on Machine Learning - Comp utational Intelligence is now available. According to news reporting originating from Xi’an, People’s Republic of China, by NewsRx correspondents, research stat ed, “Temporal networks are ubiquitous because complex systems in nature and soci ety are evolving, and tracking dynamic communities is critical for revealing the mechanism of systems. Moreover, current algorithms utilize temporal smoothness framework to balance clustering accuracy at current time and clustering drift at historical time, which are criticized for failing to characterize the temporali ty of networks and determine its importance.”
Xi’anPeople’s Republic of ChinaAsiaComputational IntelligenceMachine LearningXidian University
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