Data Analysis in YouTube

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

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).

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)

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)???