Pattern-based Graph Summarization

报告题目：Pattern-based Graph Summarization

报告人：宋骐 中国科学技术大学教授、博导

报告时间：2024年9月27日15:30 - 17:00

报告地点：秀山校区图书馆一楼报告厅

报告对象：二肖二码长期免费公开研究生、本科生及其他感兴趣师生

主办单位：二肖二码长期免费公开

报告人简介：

Qi Song is a (pre-tenure) professor at University of Science and Technology of China. Before joining USTC, he was an Applied Scientist at Amazon.com. He obtained his Ph.D. from the Database Group at Washington State University under the supervision of Prof. Yinghui Wu. His research spans the areas of Big Data, databases systems and data management, with emphasis on dynamic graphs, knowledge graph, graph query models, distributed graph processing, graph neural network and multi-model learning.

报告内容简介：

Given a set of node groups in a graph (e.g., gender or race groups), how to succinctly summarize their neighbors, and meanwhile ensure a “fair” representation to mitigate under- or over-representation of a certain group? We propose a novel framework to compute concise summaries of node groups with fairness guarantees. 1) We introduce a class of reduced summaries and study a diversified graph summarization problem, along with a new parallel algorithm with quality guarantees, 2) we formulate the fair group summarization problem and present approximation algorithms that can generate r-summaries with (a) guaranteed quality and coverage properties, and (b) relative approximations on optimal edge correction costs.