1. Data Analysis in YouTube

2. Introduction Social network + a video sharing media – Potential environment to propagate an influence. Friendship network and subscribers network – Friendship network : undirected graph – Subscribers network : directed graph

3. Data collecting Why Crawling in the social media? – The prospective graph is large and dynamic. Writing script for crawling vs. YouTube API – Application program interface(API) : public web interface provided by Google – Private group can not be crawled. Snow-ball sampling – a kind of BFS – Focus on WCC (weakly connected component) – Does not contain isolated nodes and nodes in large WCC This fraction is not large. Two hops are considered. – Measurement shows after three hops, averagely, videos are propagated through other social media like Facebook ( more exact depends on application).

4. Interaction based measurement Passive user – Users who are not making much content (like comments, content generation), can not be influential. It is valid even for friends and friends of friends. – Should be removed in sampling or modeled with small weight. Weighted graph – Or if some subscribers makes more comments, showing that influence should be more strong. Edge with high weight (e.x function of mutual interaction)

5. Modeling 1- How we can fit the obtained graph (through measurement) into the popular random network model? – Power law network – Scale-free network High degree node tend to be connected to other high degree node. – Small-world network Small diameter and high clustering 2- we can propose our ideas and test those ideas over real data. – E.x. propagation or influence is the function of degree or degree of friends or friends of friends (in-degree or out-degree)???