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Influence maximize problem with IC model (degree discount IC , new Greedy) and LT model (SIMPATH)

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README
========  
王戈扬  
## Describe  
Kempe et al formulated influence maximization as a discrete optimization problem: Given a directed social graph with users as nodes, 
edge weights reflecting influence between users, and a number k (called budget), find k users, called the seed set, such that by activating
them, the expected spread of the influence (just spread for brevity) according to a propagation model is maximized. The influence diffusion
process unfolds in discrete time steps, as captured by the propagation model. Two classical propagation models are the Linear Threshold 
(LT) Model and the Independent Cascade (IC) Model, both taken from mathematical sociology. In both models, at any time step, a user is 
either active (an adopter of the product)  
## How to use:  
see the document *Project3 - Influence Maximization Problem*  
*warning:*  
the test data must have the formet reqiured in that document(see network.txt). the node index *can not* extend the total node number.
## Preliminaries  
*Software:* Pycharm  
*Language:*  python 2.7.14  
*Algorithm Used:*  
1.SIMPATH (LT)  
2.NewGreedyIC (IC)  
3.DegreeDiscountIC (IC)  
4.SingleDiscount (LT)  
## Methodology  
### SIMPATH  
SIMPATH is an algorithm for the LT model. It uses two novel optimizations to reduce the number of spread estimation calls. 
The first one is called *Vertex Cover Optimization*,it divided the graph into two parts, a vertex cover and the rest part.we need 
to compute the spread of every node in the vertex cover and simultaneously compute the spread of the rest part of nodes. Another 
Optimization is called *Look Ahead Optimization* which addresses this issue and keeps the running time of subsequent iterations small. 
Specifically, using a parameter l, it picks the top-l most promising seed candidates in the start of an iteration and shares the marginal 
gain computation of those candidates.  
## New GreedyIC  
NewGreedyIC is an optimization of the General greedy algorithm. The core idea is
to generate a subgraph with a subset of edges. Each edge will be keeped with 
the probability equals the weight of the edge. A single subgraph can be used to
compute marginal gain of every node. Instead of resampling to calculate each 
marginal gain of a node.  
## DegreeDiscount & DegreeDiscountIC  
DegreeDiscountIC is a heurisitics algorithm for the IC model. DegreeDiscount is 
a (bad) gheurisitics algorithm for every model. we may think that nodes with 
more outdegree are more like to become the seed. These algorithm are 
essencially doing the same thing in a more accurate way. The core idea is 
that when a neighbor of a node u becomes a seed. The gian of choosing the 
ndoe u to will decress. In the DegreeDiscount algorithm it will decress by
[1]. and in the DegreeDiscountIC algorithm it will be calculate by a estimation.  
## Empirical Verification  
I used the given data to estimate the performance of my algorithm. 
THe SIMPATH algorithm is very fast and accurate. 
but the NewGreedyIC will be slow. In order to find 4 seeds in network, 
The SIMPATH,DegreeDiscountIC algorithm will get the result within 0.1 sec. 
but the NewGreedyIC will cost about 11 sec. I don't have time to do other test.   
## References  
[1] W. Chen, Y. Wang, and S. Yang, “Efficient influence maximization in social networks,” in KDD 2009.  
[2] Amit Goyal, Wei Lu, Laks V. S. Lakshmanan,SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model in IEEE 2011.

## 写给学弟学妹  
欢迎大家参考，但是请不要抄袭！

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