Information Diffusion Models
Information Diffusion Categories
Influence Models （影响力模型）
- Influence models model how users influence each other in a network.
- Nodes are in two status:
Inactive:the nodes do not receive the information
Active:the nodes have received the information and can spread it to their neighbors
Independent Cascade (IC) 独立级联模型
- Nodes activated at time t,have a single chance,at time step t+1,to activate their neighbors
- Assume v is activated at time t,for any neighbor w of u,there is a probability p(vw) that node w gets activated at time t+1
- IC Model:Algorithm
- IC Model:Example
Linear Threshold (LT) 线性阈值模型
- At each time step，the active nodes can influence the inactive nodes
- Each node has an activation threshold
- An inactive node becomes active if the sum of influence degrees exceeds its threshold
- LT Model:Algorithm
- LT Model:Example
Infection Models （感染性模型/病毒模型）
- Infection Models, also called epidemic models, are
used to describe the transmission of communicable
disease through individuals - Nodes are in three status:
- Susceptible:a susceptible node can potentially get
infected by the disease - Infected: an infected node has the chance of infecting
susceptible neighbors. - Recovered: These are nodes who have recovered from
the disease and hence have complete or partial immunity against the infection.
- Susceptible:a susceptible node can potentially get
Susceptible-Infected (SI)
- Two status of nodes：
- Susceptible（S）
- Infected（I）
- How to infect
- Once a node is infected，it stays infected forever.
- At each discrete time step，each infected node tries to infect its susceptible（uninfected）neighbors independently with probability p.
- SI Model:notations
- N:size of the crowd，N=S(t)+I(t)
- S(t)：number of susceptible ones at time t
- I(t)：number of infected ones at time t
- SI Model:Example
Susceptible-Infecte-Recovered (SIR)
- Intuition:Some infected nodes may recover，and the recovered ones can no longer get infected and are no longer susceptible.
- Three status of nodes:Susceptible（S），Infected（T），Recovered（R）
- The infection status of a node changes
β defines the probability of a success infection
γ defines the recovering probability of an infected individual - SIR Model:Example
Susceptible-Infected-Susceptible (SIS)
- Intuition:the infected nodes may recover，and the recovered nodes would become susceptible again
- Two status of nodes:Susceptible（S），Infected（I）
- The infection status of a node changes
β defines the probability of a success infection
γ defines the recovering probability of an infected individual - SIS Model:Example
Susceptible-Infected-Recovered-Susceptible (SIRS)
- Intuition:the individuals who have recovered will lose immunity after a certain period of time and will become susceptible again.
- The infection status of a node changes
β defines the probability of a success infection
γ defines the recovering probability of an infected individual
λ defines the probability of losing immunity for a recovered node - SIRS Model:Example
以上内容出自小象学院