Algorithms and Data Structures (ECE 345) ASSIGNMENT 3


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Algorithms and Data Structures (ECE 345) ASSIGNMENT 3Algorithms and Data Structures (ECE 345)
1. Assignments must be submitted by 5:00PM EST on the due date through the Quercus submission system as a single
PDF file.
2. Assignments must be completed individually except the programming exercise for which you can work in group
of up to two students. Report for the programming exercise must be submitted separately at the same time. Only
one report is needed for each group.
3. All pages must be numbered and no more than a single answer for any question.
4. Use LATEX or Microsoft Word for your assignment writeup. You can find a LATEX and a Word template for writing
your assignment on Quercus.
5. Any problem encountered with submission must be reported to the head TA as soon as possible.
6. Unless otherwise stated, you need to justify the correctness and complexity of algorithms you designed for the
EXERCISE 1 Amortized Analysis, 10 points
Consider the priority queue data structure as discussed in CLRS(Chapter 6.5). We implement a new operation DELET E(k)
which removes the k largest elements from the priority queue. Use the accounting method to show that DELET E(k) can
be done in amortized time O(k) while maintaining an amortized time O(logn) for HEAP_INSERT. Describe your implementation for DELET E(k). Do not change the implementation of the other priority queue routines.
Hint: what amortized time bound can you obtain for HEAP_EXT RACT_MAX?
EXERCISE 2 Graph, 15 points
Given a set V of objects with a “distance” function d : V ×V → R
+, with d(v, v) = 0 and a parameter k, we seek to group
V into k “clusters” (that is, partition V into k disjoint sets): V1,V2,…,Vk such that the minimum distance between any pair
of objects in two different clusters is maximized. Describe an algorithm to solve this problem and justify the correctness
and complexity of this algorithm.
Hint: Construct a complete graph with the distance function.
EXERCISE 3 Topological Sort, 15 points
Alien has came to Earth and learned the English alphabet in order to communicate with humans! However, they took the
order of letters differently and it is unknown to you. Given a set of strings consisting of unique words from this Alien
language that are sorted lexicographically, describe an algorithm that uses topological sort to find the order of all unique
letters appeared in the set.
words = [“wrt”,”wrf”,”er”,”ett”,”rftt”]
order of letters: w < e < r < t < f
Hint: A string s is lexicographically smaller than a string s
if at the first letter where they differ, the letter in s comes
before the letter in s
in the alien vocabulary. If the first MIN(LEN(s),LEN(s
)) letters are the same, then s is smaller if
and only if LEN(s) < LEN(s
EXERCISE 4 Shortest Path, 20 points
Assume there is a directed graph G = (V,E) with edge weights wi ∈ {1,…,M} and M is a constant in this case. Describe
an algorithm with time complexity of O((V + E)logM) using Dijkstra’s algorithm as basis. In other words, making
the Dijkstra’s algorithm more efficient by leveraging the fact that M is a constant through changing its underlying data
structure without changing its outline.
EXERCISE 5 Programming Exercise, 30 points + 20 bonus points
Motivated by the ever growing size of social networks, and the abundance of data describing the underlying interactions
and relationships in these networks, researchers in academia and industry have been studying various problems in this
domain. Examples of such problems include network diffusion and influence maximization. Network diffusion is the
process through which information is propagated over a network, where information can be in the form of a virus spreading
across a population, an opinion emerging over a social network, or the adoption of a recently deployed product. The
literature is rich with models that capture the dynamics of information propagation over networks [1].
In this exercise we will discuss the problem of influence maximization, which has received great interest over the last
decade. In its simplest form, influence maximization is the problem of identifying those few individuals that play a
fundamental role in maximizing the spread of information among users of a network. In past work, Domingos and
Richardson [2] were the first to pose influence maximization as one of the quintessential algorithmic problems in network
diffusion systems. Given a social graph along with estimates on how individuals influence each other, the goal is to find
these individuals that should be initially targeted by a marketing campaign so that a new product receives the largest
possible adoption rate in the network. To identify these so called “seeds”, the authors in [2] propose heuristic algorithms
and apply them on a probabilistic model of member interactions. In their seminal paper, Kempe et al. [3] formulated -for
the first time- the influence maximization problem as a constrained discrete maximization problem, and they proposed
two basic diffusion models, namely, the independent cascade (IC) model and the linear threshold (LT) model.
In this exercise we will experiment with a simplified version of the IC model in the continuous-time domain. To do so,
we first need to understand how influence spreads across nodes in a network.
Without loss of generality we will assume that networks in this study correspond to social networks. A social network
(network) is modelled as a finite directed graph G = (V,E), where each node u ∈ V corresponds to a user in the network,
and each edge (u, v) ∈ E implies a social connection (dependence) between users u and v. For example, a directed edge
(u, v) may correspond to the fact that user u is followed by (or is a friend of) user v. If there exists an edge (u, v) ∈ E then
we also say that v is a neighbour of u. Along these lines, the set of all neighbours of u is denoted as N(u).
The IC Model (simplified) In this model every edge (u, v) ∈ E is assigned with some weight tuv ∈ R+. We say that node
u influences node v ∈ N(u) after time tuv. Once node v becomes influenced by u the spread of influence continues by
v influencing its neighbours, then v’s neighbours influencing their neighbours and so on. Once a node is influenced it
remains in that state and cannot be influenced again. It becomes apparent that the further apart two nodes are in the graph
the longer it will take for one to influence another. Also, if node v is unreachable from u in G, then u can never influence
v through the process above.
Often, in real-world marketing scenarios we are interested in computing the spread of influence within specific “deadlines”. A marketer usually wants to estimate the adoption that a campaign achieves within a few hours or days rather than
in a few years. As such we have an external positive constraint, referred to as the “deadline”, which is denoted as T ∈ R+.
This deadline essentially tells us up to what point in time we are interested to measure the spread of influence, and it gives
rise to a very useful property of the IC model:
The Shortest Path Property The shortest path property says the following: if s(u, v) is the length of the shortest path
from node u to node v, then u influences node v after time s(u, v). Given deadline T we say that u influences v within time
T if and only if s(u, v) ≤ T.
Now we can define the spread of a node u given deadline T as the number of nodes that u can influence within deadline
T. We denote the spread of u as σ(u), and we formally have:
σ(u) = |{v ∈ V : s(u, v) ≤ T}| (5.1)
Note that node u by definition influences itself, since s(u,u) = 0 and T ≥ 0 always.
With this setting, one interesting task is to identify, for a given T, which node in G is the most influential. That is, which
node has maximum spread over all other nodes. We usually refer to this node as the Top-1 influencer.
Computing the Top-1 Influencer (30 points)
The task is simple:
1. Pick a node u ∈ V and run shortest paths with u as the source
2. Enumerate how many nodes in V have s(u, v) ≤ T. This is the spread of u.
3. Initialize all distances back to ∞ and repeat Step 1 and 2 for every other node in V
4. Return the node with maximum spread. If there are ties, break them randomly.
Note that in Step 3 above we first initialize every distance back to ∞ and then run shortest paths again.
Identifying the Top-1 influencer is important for a campaign but is rarely enough to achieve a good spread across the
network. The more influencers we identify the better the spread of influence usually becomes. That is why we usually
talk about the Top-K influencers (if our marketing budget allows us to initially target these K individuals with ads, personal
contact etc). Here we will just focus on finding the Top-2 influencer. You might think that the Top-2 influencer is the one
with the second best spread in the process we described above, but that is not the case. Imagine that the node with second
best spread achieves 99% of the spread of the Top-1 influencer, but all the nodes that it influences are already nodes that
the Top-1 influencer can reach by itself. There wouldn’t be any reason to use the node with second best spread in that first
round of computations due to this redundancy. This is where the notion of marginal spread gain comes into play. To
understand it we first need to define the spread of two nodes.
The spread of two nodes u and w given deadline T is the number of nodes that u OR w can influence within deadline T.
We denote the spread of u and w as σ(u,w), and we formally have:
σ(u,w) = |{v ∈ V : [s(u, v) ≤ T]∨[s(w, v) ≤ T]}| (5.2)
Now that we know how to compute the spread of two nodes, we can define the marginal spread gain of node w as
σ(u,w)−σ(u). The marginal spread gain essentially gives us “the number of extra nodes that we can influence if we use
w along with u”. In our case, the Top-2 influencer is the one that will maximize the marginal gain when added to the
Top-1 influencer. Finally note that σ(u,w) − σ(u) ≥ 0, since you cannot influence fewer nodes if you use an additional
Computing the Top-2 Influencer (20 points, bonus)
1. Run shortest paths with the Top-1 influencer as the source, and mark all nodes influenced within T as influenced
2. Pick a node w ∈ V other than the Top-1 Influencer and run shortest paths from w
3. Enumerate how many nodes in V have s(w, v) ≤ T and are not already marked as influenced. This is the marginal
spread gain of w.
4. Initialize all distances back to ∞ and repeat Step 2 and 3 for every other node in V other than the Top-1 Influencer
5. Return the node with maximum marginal spread gain. If there are ties, break them randomly.
In this exercise you will implement the above two processes for finding the Top-1 and Top-2 influencers in a directed
graph that represents a social network.
Your executable will be named influence. It will be given an input file named graph which represents edges and edge
weights between nodes. It will be a 3-column file and each row will be of the format:
node u node v weight
The row above indicates the existence of a directed edge (u, v) in G with weight tuv = weight. Entries are separated by
“space”. For simplicity node names will be a 0,…,|V| −1 enumeration, if |V| is the number of nodes, and indexing will
start at 0 always. Finally, you may assume that weights will be randomly and uniformly chosen from the range (0,5].
There can be cycles in the graph but there will be no zero or negative edge weights. For example consider the following
0 2 1.5
1 2 0.4
1 3 2.9
2 3 3.3
3 5 0.1
3 4 2.4
4 1 0.7
5 6 0.7
The above file corresponds to the graph in Figure 1. Your program will also take as input a deadline constraint T, which
you may assume will be a positive real number in the range [1,10].
To summarize, your executable should be callable from command line as:
influence graph T
Figure 5.1: Example Graph
Your program will compute the Top-1 and Top-2 Influencers by following the process described above. Specifically you
will have to compute and output the following:
1. The Top-1 Influencer, its spread, and the time taken to find the Top-1 Influencer (you will measure time for all
computations including creating the graph, running shortest paths and finding the max spread)
2. The Top-2 Influencer, its marginal spread gain, and the time taken to find the Top-2 Influencer (here you will
measure the time for all computations that take place AFTER the Top-1 Influencer is found)
Your program will print these results to standard output as follows:
TOP-1 INFLUENCER: (node name), SPREAD: (spread), TIME: (time)
TOP-2 INFLUENCER: (node name), MARGINAL SPREAD: (spread), TIME: (time)
In the example of Figure 1, if the program is run with T = 3 the output should be:
You can verify this by checking that there are 4 nodes with their shortest path from node 1 being ≤ T. Particularly, nodes
1,2,3 and 5. It is also the case that there are 4 nodes with their shortest path from node 3 being ≤ T (nodes 3, 4, 5 and 6).
Remember you always include the influencers in their spread unless they are already marked as influenced by a previous
one. Thus, nodes 1 and 3 have spread 4 which is actually the max spread. Randomly break the tie and pick node 1 as
the Top-1 Influencer. Now note that node 3 has marginal spread 2 because adding it to node 1, allows nodes 4 and 6 to
be influenced as well. Strictly speaking, σ(1,3)−σ(1) = 6−4 = 2. You can verify that the marginal spread of the other
nodes is smaller than 2. Therefore, node 3 is the Top-2 Influencer. Note that the times in this example are fictional.
• Your source code, including a full build environment and instructions how to build in the configuration TOML file.
Please follow a similar style as provided with the A2 coding problem.
• You are encouraged to use external libraries but you need to make sure to include any libraries in your submission
so that we can compile the code.
• A written report as part of your Assignment 3 submission, where you address the following points:
– Implementation details: (a) how are you representing the graph, using an adjacency matrix or an adjacency
list, and why? (b) Which shortest path algorithm did you use and why?
– You will need to use multiple graph files of your making (you will be provided a large file as well) to run
the following experiment: create 10 different files such that all have 100 nodes, but with different density
= (# edges)/(# nodes). For example, a graph (file) with 100 nodes and 300 edges (rows) has density 3.
Density should range between 2 and 10. Run your code and show the results in a time vs. density plot. When
we refer to time we mean the total time taken to compute the two top influencers (i.e., 3.7 sec in the example
– Discuss the plot above. What do you observe and why?
[1] M. Jackson. “Social and Economic Networks” Princeton University Press, Princeton and Oxford, 2008
[2] P. Domingos, M. Richardson. “Mining the Network Value of Customers,” in International Conference on Knowledge
Discovery and Data Mining (KDD), 2001
[3] D. Kempe, J. M. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in International Conference on Knowledge Discovery and Data Mining KDD, 2003

Algorithms and Data Structures (ECE 345) ASSIGNMENT 3
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