【读书笔记】Community Detection and Mining in Social Media（二）

Nodes, Ties and Influence

————————————————————————IMPORTANCE OF NODES————————————————————————

并不是所有的节点都是同等重要的

• Centrality Analysis:

　　　　Find out the most important nodes in one network

• Commonly-used Measures

　　　　Degree Centrality

　　　　　　　　The importance of a node is determined by the number of nodes adjacent to it

　　　　　　　　　

　　　　Closeness Centrality

　　　　　　　　Importance measured by how close a node is to other nodes

　　　　　　　　

　　　　Betweenness Centrality

　　　　　　　　

　　　　Eigenvector Centrality

　　　　　　　　

 

—————————————————————————STRENGTHS OF TIES—————————————————————————

也并不是所有的链接都是同等重要的

Connections in Social Media

Imperative to estimate the strengths of ties for advanced analysis

　　　　Learning from Network Topology

　　　　　　　　

　　　　　　　　

　　　　　　　　

　　　　Learning from Profiles and Interactions

　　　　　　　　

　　　　Learning from User Activities

　　　　　　　　

 

————————————————————————INFLUENCE MODELING—————————————————————————

Linear threshold model (LTM)
Independent cascade model (ICM)

　　　　　　　　

　　　　　　

　　　　　　

　　　　　　

　　　　　　

————————————————DISTINGUISH BETWEEN INFLUENCE AND CORRELATION————————————————

Correlate

　　　　It has been widely observed that user attributes and behaviors tend to correlate with their social networks

　　　　　　Suppose we have a binary attribute with each node (say, whether or not being smoker)
　　　　　　If the attribute is correlated with the network, we expect actors sharing the same attribute value to be positively correlated with social connections
　　　　　　That is, smokers are more likely to interact with other smokers, and non‐smokers with non‐smokers

 

　　　　If the fraction of edges linking nodes with different attribute values are significantly less than the expected probability, then there is evidence of correlation