CCSS Workshop on Computational Social Science
Program
CCSS Workshop on Computational Social Science
計算社会科学研究センター主催、RIEBセミナー／科研基盤研究（A）#21H04595 共催
Hosted by Center for Computational Social Science of Kobe University, Jointly Supported by RIEB Seminar / Grant-in-Aid for Scientific Research (A) #21H04595
| 日時 Date |
2023年2月10日（金）11:30 ～ 13:00 (Friday, February 10, 2023, 11:30 - 13:00) |
|---|---|
| 会場 Venue |
経済経営研究所 調査室（兼松記念館1階） Seminar Room at RIEB (Kanematsu Memorial Hall, 1st Floor) |
| 対象 Inteded Audience |
教員、院生、および同等の知識をお持ちの方 Faculty, Graduate Students, and People with Equivalent Knowledge |
| 使用言語 Language |
英語 English |
11:30～13:00
- 論題
Topic - A Generalized Hypothesis Test for Community Structure in Networks (co-author with Srijan Sengupta)
- 報告者
Presenter - Eric YANCHENKO（Department of Statistics, North Carolina State University）
- 概要
Abstract - Researchers theorize that many real-world networks exhibit community structure where within-community edges are more likely than between-community edges. While numerous methods exist to cluster nodes into different communities, less work has addressed this question: given some network, does it exhibit statistically meaningful community structure? We answer this question in a principled manner by framing it as a statistical hypothesis test in terms of a general and model-agnostic community structure parameter. Leveraging this parameter, we propose a simple and interpretable test statistic used to formulate two separate hypothesis testing frameworks. The first is an asymptotic test against a baseline value of the parameter while the second tests against a baseline model using bootstrap-based thresholds. We prove theoretical properties of these tests and demonstrate how the proposed method yields rich insights into real-world data sets.