ContexTour: Contextual Contour Visual Analysis on Dynamic Multi-Relational Clustering

論文發表人: 林育如 (美國亞利桑那州立大學/資訊科學系)

http://www.siam.org/meetings/sdm10/

 

社群網站上,  日積月累形成意義豐富的社會網絡資料. 這些動態 多重關係的網路令使用者難以了解隱含於社群媒介的人際模式, 在此研究中, 我們提出ContexTour– 可讓使用者探索社群活動及其演化的多重維度視算模式. ConextTour包含兩個運算模組: 第一, 動態式多重網絡群聚運算—它有效地追蹤群組演化並兼顧群組變化的連續性; 第二, 動態式網絡等高圖視算—將群組活動及其演化的多面向視覺化.  我們透過DBLP資料, 以量化及個案的方式來檢驗並展示ContexTour的優越效能. 在量化檢驗上, ContextTour  比過去的基準方法, 提升了85到165倍的效能. 個案檢驗上, 視算結果展示我們的方法合理地呈現了資訊研究社群十年內的演變過程.

 

Huge amounts of rich context social network data are generated everyday from various applications such as FaceBook and Twitter. These data involve multiple social relations which are community-driven and dynamic in nature. The complex interplay of these characteristics poses tremendous challenges on the users who try to understand the underlying patterns in the social media. We introduce an exploratory analytical framework, ContexTour, which generates visual representations for exploring multiple dimensions of community activities, including relevant topics, representative users and the community-generated content, as well as their evolutions. ContexTour consists of two novel and complementary components: (1) Dynamic Relational Clustering (DRC) that efficiently tracks the community evolution and smoothly adapts to the community changes, and (2) Dynamic Network Contour-map (DNC) that visualizes the community activities and evolutions in various dimensions. In our experiments, we demonstrate ContexTour through case studies on the DBLP dataset. The visual results capture interesting and reasonable evolution in Computer Science research communities. Quantitatively, we show 85-165X performance gain of our DRC algorithm over the baseline method.