1. Overview In this project, you have two computational tasks for Influence Maximization Problems (IMPs) in social networks. The first is to implement an estimation algorithm for the influence spread and the second is to design and implement a search algorithm for IMPs. An introduction to IMP (including problem formulations and influence spread definition) can be found in the package we provide (IMP.pdf). The IMP is NP-hard and the influence spread computation is #Phard under the definitions shown in the introduction. Overall, your estimation algorithm needs to give a good estimation of the influence spread and your search algorithm needs to find a high-quality solution to IMPs as fast as possible within a limited time. The scores you get in this project will be given according to the performance of your algorithms in our test. 2. General rules After your submission, we will test your influence spread estimator and IMP solver on different IMP problem instances. To make this process as smooth as possible, the package you submit must satisfy the following requirements. a) Algorithm description i. The description of your algorithms should be submitted in pdf format, in which you should describe the core idea of your design and give the pseudo code. b) Programming aspects i. In order to get rid of the operating system related issues and the execution efficiency issues of different programming languages, your algorithm must be implemented using Python 2.7 (Python 3.0+ not included) and the only allowed library is numpy. ii. The executable estimator must be named by ISE.py and the executable solver must be named by IMP.py c) Task 1: influence spread computation i. Input: the format of the estimator call should be as follows: python ISE.py –i -s -m -b -t