상세정보
Export to Refworks부가기능
Extracting Insights from Differences: Analyzing Node-Aligned Social Graphs
상세 프로파일
| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | Extracting Insights from Differences: Analyzing Node-Aligned Social Graphs. |
| 개인저자 | Datta, Srayan. |
| 단체저자명 | University of Michigan. Computer Science & Engineering. |
| 발행사항 | [S.l.]: University of Michigan., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 180 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-02B. Dissertation Abstract International |
| ISBN | 9781085668910 |
| 학위논문주기 | Thesis (Ph.D.)--University of Michigan, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-02, Section: B.
Advisor: Adar, Eytan. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | Social media and network research often focus on the agreement between different entities to infer connections, recommend actions and subscriptions and even improve algorithms via ensemble methods. However, studying differences instead of similarities can yield useful insights in all these cases. We can infer and understand inter-community interactions (including ideological and user-based community conflicts, hierarchical community relations) and improve community detection algorithms via insights gained from differences among entities such as communities, users and algorithms. When the entities are communities or user groups, we often study the difference via node-aligned networks, which are networks with the same set of nodes but different sets of edges. The edges define implicit connections which we can infer via similarities or differences between two nodes.We perform a set of studies to identify and understand differences among user groups using Reddit, where the subreddit structure provides us with pre-defined user groups. Studying the difference between author overlap and textual similarity among different subreddits, we find misaligned edges and networks which expose subreddits at ideological 'war', community fragmentation, asymmetry of interactions involving subreddits based on marginalized social groups and more. Differences in perceived user behavior across different subreddits allow us to identify subreddit conflicts and features which can implicate communal misbehavior. We show that these features can be used to identify some subreddits banned by Reddit. Applying the idea of differences in community detection algorithms helps us identify problematic community assignments where we can ask for human help in categorizing a node in a specific community. It also gives us an idea of the overall performance of a particular community detection algorithm on a particular network input. In general, these improve ensemble community detection techniques. We demonstrate this via Community-Diff (a community detection and visualization tool), which compares and contrasts different algorithms and incorporates user knowledge in community detection output. We believe the idea of gaining insights from differences can be applied to several other problems and help us understand and improve social media interactions and research. |
| 일반주제명 | Computer science. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
