A graph based action network framework to identify prestigious members through
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This study proposes a graph-based framework to identify prestigious members on social media platforms by analyzing their prestige evolution over time. The framework establishes behavior networks from user interactions over 10 time periods on Flickr and analyzes degree distribution, in/out degree correlation, and mixing patterns to identify currently active members. It then predicts future activity and prestige using four indicators: homophily, triadic interaction rules, continuous interests, and recency effects. Prestige is calculated based on favor volume, coverage, and timeliness. The framework extends to predicting future prestige evolution.
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A graph based action network framework to identify prestigious members through
- 1. A graph-based action network framework to identify prestigious members through member's prestige evolution Dongyuan Lu ,Qiudan Li ,Stephen Shaoyi Liao 報告者 陳柏宇
- 2. 研究介紹 研究目的 人們常常會從有聲望的會員中得到有用的訊息，而公司在找尋 這樣的人，增加他們的網路口碑。而這個研究主要提出一個方 法。找出潛在有聲望的會員。 資料來源 Flickr API: 50173 相片,3875擁有者,0.25million加入最愛記錄 研究設計 提出三種方法，找出現在活躍的使用者；以及四個因素：同質 性、愛屋及烏、喜好不變、時間遠近，調整使用者行為網路的 連結權重，並預測使用者未來的活躍值。 主要發現 1.互相將照片加入最愛的人是會更傾向將彼此的照片加入最愛 2.使用者的同意行為遵守愛屋及烏規則 3.使用者間過去的互動記錄影響現在的評分 4.現在的加入最愛行為比以前的行為更具代表性 最後結論 提出四個指標預測使用者將照片加到最愛的行為，並且根據這 個指標以一套有效的框架能夠加上預測使用者未來的互動，進 而求出潛在的活躍會員。
- 3. Overview
- 4. Step1 • 根據使用者的互動建立十個不同時間的行 動網路圖(G -G )，並且將這十個再合成一個 1 10 圖(G ) 1-10
- 6. Step2 • 對行動網路圖G10，用三種方法分析資料， 找出現有資料中的活躍會員是否與其他一 般使用者有明顯的區別
- 7. Step 2-1 • Degree distribution – If a large number of members are with low degree and only a few members are with high degree, we can verify the existence of highly interconnected “central” members, but also indicate the preference behaviour of “new-arrival” members.
- 8. Step 2-2 • Out degree and indegree correlation – If the correlation is weak and the outdegree is higher than indegree for most members, it suggests the communication behaviour of “non- central” members turns out to favor members highly interconnected.
- 9. Step 2-3 • Mixing pattern – measure the probability of a member with degree k preferring to connect to a member with degree out k. in – a positive value of assortativity coefficient further confirm the communication behaviour of “non- central” members and also confirm the existence of “central-like” prestigious members.
- 11. Step3 • 預測未來活躍會員的指標 • Homophily – members with similar interests are more likely to appreciate each other's photos in future. • The Triadic interaction rules – “the favourites of my favored people are also my favourites.
- 12. Homophily
- 13. The Triadic interaction rules
- 14. Step3-2 • The continuous nature of interests – One would probably favor the same members over a short period • Racency Nature – Since members‘ activities evolve over time, it is quite likely the more recent favor action constitutes a heavier indicator for prediction
- 15. The continuous nature of interests
- 16. Step4 • 計算聲望 • Volume – the number of photos of a member chosen as favourites. • Coverage – not only reflected by the number of fans of a member, but also takes into consideration the initial influential degree of these fans. • Timeliness – Time sensitive favor actions a member receives.
- 17. Step4-2 Favor volume Favor coverage
- 18. 聲望計算
- 19. Step5 • 預測未來的聲望 • Extend common neighbor • Varience of KZ Algorithm
- 22. Conclusion • we propose a way to address the problem of predicting evolution of prestigious members using quality indicator Flickr groups as a testbed. • we present four indicator features for predicting favor action intentions of members.