报告人
高敏 复旦大学 时间 2025年11月24日 星期一 下午 13:00-14:00 地点
308会议室
Abstract Networks are effective for representing relationships between entities across a range of disciplines, and network analysis techniques are widely used for understanding various types of complex networks, e.g., social networks, biological networks, transportation networks. Network analysis tasks, such as community detection, centrality analysis, and network visualization, play important roles in many disciplines. Existing network analysis tools, however, lack efficiency in analyzing massive network data or may not provide comprehensive analysis functions, which limits their practical applicability. We present EasyGraph, an open-source library that supports many network data formats and covers important functions like structural hole spanner detection and network embedding. Notably, we have optimized several key functions for enhanced efficiency. We believe that EasyGraph is a powerful tool for dealing with major analytical tasks in complex networks across various domains.
Biography 高敏，复旦大学博士研究生，师从陈阳教授。主要研究方向包括社交网络分析、图学习及相关应用（例如欺诈检测、社交机器人检测等）、图和LLM等。代表性工作包括高效的网络分析开源库EasyGraph。相关成果发表于Patterns （Cell Press)， Humanities and Social Sciences Communications (Nature Portfolio)， TNSE， CIKM，FITEE等国际会议和期刊上。
