Parallel Algorithm for graphs modeling social networks

The goal of the project is to implement for graphs modeling social networks

enumerating algorithms for communities (i.e. sets of densely connected vertices).

connected vertices). This kind of algorithm has many practical applications, for example for

advertising targeting. For this project, we will use the approach proposed in the scientific paper [1].

In particular, let a graph G = (V, E) with V the set of vertices and E the set of edges

of G. We represent a community by a clique. A clique of G is a set K of

vertices of the graph all connected two by two. The clique K is maximal if there is no

vertex in V ∖ K connected to all the vertices of K. The problem we consider is

to enumerate all maximal cliques in a graph. In a first step, you will implement

the two sequential algorithms presented in [1]. These algorithms are parameterized by the

degeneracy of the input graph, which you will have to compute. You will use the

Bron-Kerbosch algorithm as an enumeration subroutine. In a second step, you will try to

parallelize these algorithms. In all the steps of the project you will wait until the algorithms

algorithms are presented theoretically, then compared on different instances in terms of time and

execution space.

Skills: Parallel Processing, Software Development, Python

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About the Employer:
( 2 reviews ) sannois, France

Project ID: #29913486

Awarded to:


Hello I was working with such tasks before, but I can help only with C/C++ implementations using MPI/OpenMP (or hybrid). Thus If you need Python implementation, just ignore my bid. Regards

€200 EUR in 7 days
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2 freelancers are bidding on average €170 for this job


Dear sir. As an experienced Haskell, C#, C/C++, Java and Python expert, it is interesting to me. I have been working on various Haskell, C#, C/C++, Java and Python projects for 10+ years. As i having programming contes More

€140 EUR in 7 days
(7 Reviews)