Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches - Complementary Data

Description

This dataset contains complementary data to the paper "Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches" [1], which studies integer/constraint programming formulations for the Least Cost Directed Perfect Awareness Problem, an NP-hard optimization problem that arises in the context of influence marketing. Regarding the computational experiments conducted in the paper, we make available: - Two sets of instances; - The best known attained solutions; - The source code; - An appendix with additional details about the results. The first input set includes 300 synthetic instances composed of graphs that resemble real-world social networks [2]. The second set consists of 14 instances built from online interactions crawled from X (formerly known as Twitter) [3]. The directories "synthetic_instances" and "x_instances" contain files that describe both sets of instances. The first two lines of each file contain: <n> <m> where <n> and <m> are the number of vertices and edges in the graph. Each of the next <n> lines contains: <v> <c> <t> where <v> identifies a vertex while <c> and <t> are the cost and threshold associated to that vertex. Each of the <m> remaining lines contains: <u> <v> <w> where <u> and <v> identify an ordered pair of vertices that determines a directed edge with weight <w>. The directories "solutions_for_synthetic_instances" and "solutions_for_x_instances" contain files that describe the best known solutions for both sets of instances. The first line of each file contains: <s> where <s> is the number of vertices in the solution. Each of the next <s> lines contains: <v> where <v> identifies a seed vertex. The last line contains: <c> where <c> is the solution cost. The directory "source_code" contains the implementations of the mathematical models studied in the paper. Lastly, the file "appendix.pdf" presents details of the results reported in the paper [1]. This work was supported by grants from Santander Bank, Brazil, Brazilian National Council for Scientific and Technological Development (CNPq), Brazil, and São Paulo Research Foundation (FAPESP), Brazil. Caveat: the opinions, hypotheses and conclusions or recommendations expressed in this material are the responsibility of the authors and do not necessarily reflect the views of Santander, CNPq or FAPESP. References [1] F. C. Pereira, P. J. de Rezende and T. Yunes. Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches. Submitted. 2023. [2] F. C. Pereira, P. J. de Rezende. The Least Cost Directed Perfect Awareness Problem: complexity, algorithms and computations. Online Social Networks and Media, 37-38, 2023. [3] C. Schweimer, C. Gfrerer, F. Lugstein, D. Pape, J. A. Velimsky, R. Elsässer, and B. C. Geiger. Generating simple directed social network graphs for information spreading. In Proceedings of the ACM Web Conference 2022, WWW ’22, pages 1475–1485, 2022.

Steps to reproduce

First, modify the first two lines of Makefile to configure the paths to the Gurobi and CP-SAT (from Google OR-Tools) installation folders in your machine. Then, open a terminal at the "source_code" directory and execute the command 'make'. This will compile the source code and create an executable file named "program". To run the program, execute the following command in your terminal: ./program -ip <path_to_instance_folder>/<instance_name>.in -model <model_name> where <path_to_instance_folder> denotes the path to the directory containing the instance file, <instance_name> indicates the name of the instance, and <model_name> corresponds to the name of the formulation designed to solve the instance. The <model_name> argument must be IP-ROUNDS, IP-ARCS, IP-ARCS-POLY, IP-ORDERING, CP-ROUNDS, CP-ARCS-POLY or CP-ORDERING. In the end of the execution, the program will write the best feasible solution found in a file named <instance_name>.sol. The following arguments are optional: -time_limit : runtime limit in seconds (default 3600s); -threads : maximum number of threads of execution (default 1); -initial_solution_path : path to a .sol file containing the description of a feasible solution; -lower_bound : a lower bound value for the objective function; -upper_bound : an upper bound value for the objective function; -print_instance : set 1 for printing a description of the instance in the beggining of the execution (default 0).

Institutions

Categories

Funding

Licence