Recent progress on random graph matching problems

：丁剑（北京大学）
：2024-11-09 09:00
：海韵园行政楼C503

报告人：丁剑（北京大学）

时  间：2024年11月9日9:00

地  点：海韵园行政楼C503

内容摘要:

A basic goal for random graph matching is to recover the vertex correspondence between two correlated graphs from an observation of these two unlabeled graphs. Random graph matching is an important and active topic in combinatorial statistics: on the one hand, it arises from various applied fields such as social network analysis, computer vision, computational biology and natural language processing; on the other hand, there is also a deep and rich theory that is of interest to researchers in statistics, probability, combinatorics, optimization, algorithms and complexity theory.

Recently, extensive efforts have been devoted to the study for matching two correlated Erdős–Rényi graphs, which is arguably the most classic model for graph matching. In this talk, we will review some recent progress on this front, with emphasis on the intriguing phenomenon on (the presumed) information-computation gap. In particular, we will discuss progress on efficient algorithms thanks to the collective efforts from the community. We will also point out some important future directions, including developing robust algorithms that rely on minimal assumptions on graph models and developing efficient algorithms for more realistic random graph models.

This is based on joint works with Guanyi Chen, Hang Du, Yumou Fei, Shuyang Gong, Zhangsong Li and Yuanzheng Wang in various combinations.

个人简介：

丁剑，北京大学讲席教授。主要研究领域是概率论，尤其关注统计物理学与计算机科学的交叉。2011年获美国加州大学伯克利分校博士学位。曾任芝加哥大学统计系助理教授、副教授，宾夕法尼亚大学沃顿商学院副教授、Gilbert Helman 讲席教授。曾获NSF Career Award（2015），Alfred P. Sloan Fellowship (2015), Rollo Davidson Prize (2017), 华人数学家大会金奖（2022），科学探索奖（2023），Loève Prize（2023）。受邀在2022年国际数学家大会作报告，在2023年作IMS Medallion Lecture，并受邀参加2024年度国际数学物理大会(ICMP2024)作一小时大会报告。

 

联系人：周达