information cascade
DESCRIPTION
Information Cascade. Priyanka Garg. OUTLINE. Information Propagation Virus Propagation Model How to model infection? Inferring Latent Social Networks Inferring edge influence Inferring influence volume. Information Propagation. How information/infection/influence flows in the network? - PowerPoint PPT PresentationTRANSCRIPT
INFORMATION CASCADEPriyanka Garg
OUTLINE Information Propagation
Virus Propagation Model How to model infection?
Inferring Latent Social Networks Inferring edge influence Inferring influence volume
INFORMATION PROPAGATION How information/infection/influence flows in the
network? Epidemiology:
Question: Will a virus take over the network? Type of virus:
Susceptible Infected Susceptible (SIS) Example: Flu
Susceptible Infected Removed (SIR) Example: Chicken-pox , deadly disease
Viral Marketing: Once a node is infected, it remains infected. Question: How to select a subset of persons such
that maximum number of persons can be influenced?
HOW TO MODEL INFECTION? Simple model:
Each infected node infects its neighbor with a fixed probability.
SIS: A node infects its neighbor with probability b
(how infectious is the virus?) Node recovers with probability a (how easy is it
to get cured?) Strength of virus = b/a Result: If virus strength < t then virus will
instinct eventually. t = 1/largest eigen value of adjacency matrix A.
HOW TO MODEL INFECTION? Independent Contagion Model
Each infected node infects its neighboring node with probability pij.
Threshold Model Each infected node i infect its neighboring node j
with weight wij. The node j becomes active if ∑j=neigh(i)wij > thi. thi is the threshold of node i.
HOW TO MODEL INFECTION?: GENERAL CONTAGION MODEL General language to describe information
diffusion. Model:
S infected nodes tried but failed to infect node v. New node u becomes infected. Probability of node u successfully influencing node v
also depends on S. pv(u, S) Example
Node becomes active if k of its neighbors are active. ie. if |S + 1| > k then pv(u, S) = 1 else 0
Independent Cascade: pv(u,S) = p(u,v)
Threshold model: if (p(S,v) + p(u,v)) > t then pv(u,S) = 1 else 0
HOW TO MODEL INFECTION?: GENERAL CONTAGION MODEL Can also model the diminishing returns
property S>T then Gain(S + u) < Gain (T + u) Gain = Probability of infecting neighbor j
CHALLENGES IN USING THESE MODELS Problem under consideration
Viral marketing: How to select a subset of persons such that maximum number of persons can be influenced?
How to find the infection probability/weights of every edge?
INFERRING INFECTION PROBABILITIES We know the time of infections over a lots of
cascades. Train:
Maximize the likelihood of node infections over all the nodes in all the cascades.
Likelihood = ∏c∏iPi,c
Pi = P(i gets infected at time ti| infected nodes) Independent Contagion Model
Pi=At least one of the already infected node infects node i
Pi= 1 - ∏j(1-(probability of infection from node j to node i at time ti))
INFERRING INFECTION PROBABILITIES Variability with time:
Infection probabilities vary with time. Let w(t) is the distribution which captures the variability with time.
Probability of node j infecting node i at time t is w(t-tj)*Aji. Here tj is the infection time of node j.
Thus: Pi= 1 - ∏j(1- w(ti-tj)Aji)
The log-likelihood maximization problem can be shown to be a convex optimization problem
ANOTHER APPROACH: MORE DIRECT Find number of infected nodes at any time t? Number of infected nodes at time t depends
only on number of already infected nodes. Model:
V(t) is the number of nodes infected at time t V(t+1) = ∑u=1,N ∑l=0,L-1 Mu(t-l) Iu(l+1) Mu(t) = 1 if node u is infected at time t Iu(t) = Infection variability with time
Minimize the difference between V(t) and observed volume at every time t.
Accounting for novelty: V(t+1) = α(t)∑u=1,N ∑l=0,L-1 Mu(t-l) Iu(l+1)
THANK YOU
SIS Let
pit = P(i is infected at time t) tit = P(i doesn’t receive infection from its
neighbor) tit = ∏j=neigh(i) (pj(t-1) (1-b) + 1 – pj(t-1))
1-pit=P(i is healthy at t-1 and didn’t receive infection) + P(i is infected at t-1 and got recovered and didn’t receive infection) + P(i is not infected at t-1 but got cured after infection at t).
1 – pit = (1-pi(t-1)) tit + pi(t-1)a tit + (1-pi(t-1))tita 0.5