A Hybrid Matheuristic for the Spread of Influence on Social Networks - Complementary Data
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This dataset contains complementary data to the paper "A Hybrid Matheuristic for the Spread of Influence on Social Networks" [1], which proposes a matheuristic for combinatorial optimization problems involving the spread of information in social networks. For the computational experiments discussed in that paper, we provide: - Two sets of instances, originally obtained from [2-6]; - The solutions attained by exact and heuristic methods; - The collected results; - The matheuristic source code; The directories "benchmark_*/instances/" contain files that describe the sets of instances. Each instance is associated with a graph containing <n> vertices and <m> edges. The first <m> lines of each file contain: <u> <v> where <u> and <v> identify a pair of vertices that determines an undirected edge. The next line contains <n> integers corresponding to the costs of the vertices. The last line contains <n> integers corresponding to the thresholds of the vertices. The directories "benchmark_*/solutions_*/" contain files describing feasible solutions for the corresponding sets of instances. The first line of each file contains: <s> where <s> is the number of vertices in the target set. Each of the next <s> lines contains: <v> where <v> identifies a target. The last line contains an integer that represents the target set cost. The directory "hmf_source_code/" contains an implementation of the matheuristic framework proposed in [1], namely, HMF. This work was supported by grants from Santander Bank, the Brazilian National Council for Scientific and Technological Development (CNPq), the São Paulo Research Foundation (FAPESP), the Fund for Support to Teaching, Research and Outreach Activities (FAEPEX), and the Coordination for the Improvement of Higher Education Personnel (CAPES), all in Brazil. Caveat: The opinions, hypotheses and conclusions or recommendations expressed in this material are the sole responsibility of the authors and do not necessarily reflect the views of Santander, CNPq, FAPESP, FAEPEX, or CAPES. References [1] F. C. Pereira, P. J. de Rezende, and T. Yunes. A Hybrid Matheuristic for the Spread of Influence on Social Networks. 2024. Submitted. [2] S. Raghavan and R. Zhang. A branch-and-cut approach for the weighted target set selection problem on social networks. 2024. https://doi.org/10.1287/ijoo.2019.0012 [3] J. Leskovec and A. Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. 2024. https://snap.stanford.edu/data [4] R. A. Rossi and N. K. Ahmed. The Network Data Repository with Interactive Graph Analytics and Visualization. 2022. https://networkrepository.com [5] J. Kunegis. KONECT – The Koblenz Network Collection. 2013. http://dl.acm.org/citation.cfm?id=2488173 [6] O. Lesser, L. Tenenboim-Chekina, L. Rokach, and Y. Elovici. Intruder or Welcome Friend: Inferring Group Membership in Online Social Networks. 2013. https://doi.org/10.1007/978-3-642-37210-0_40
本数据集为论文《面向社交网络影响力传播的混合数学启发式算法》（A Hybrid Matheuristic for the Spread of Influence on Social Networks）[1]提供配套补充数据。该论文针对社交网络中信息传播相关的组合优化问题，提出了一种数学启发式算法（matheuristic）。 针对该论文中提及的计算实验，本数据集提供如下内容： - 两组原始源自文献[2-6]的测试用例集； - 精确算法与启发式算法所得到的求解结果； - 实验采集的全部结果数据； - 该数学启发式算法的源代码。 目录`benchmark_*/instances/`内包含用于描述各测试用例集的文件。每个测试用例对应一张包含<n>个顶点与<m>条边的无向图。 每个文件的前<m>行格式如下： <u> <v> 其中<u>与<v>为一对顶点标识符，用于确定一条无向边。 紧随其后的一行包含<n>个整数，分别对应各顶点的权重（成本）。 文件最后一行包含<n>个整数，分别对应各顶点的阈值参数。 目录`benchmark_*/solutions_*/`内包含用于描述对应测试用例集可行解的文件。 每个文件的首行格式如下： <s> 其中<s>代表目标集合中的顶点数量。后续的<s>行每行格式如下： <v> 其中<v>为一个目标顶点的标识符。文件最后一行包含一个整数，代表该目标集合的总权重（成本）。 目录`hmf_source_code/`内包含文献[1]所提出的数学启发式算法框架（HMF）的实现代码。 本研究获得巴西桑坦德银行、巴西国家科学技术发展委员会（CNPq）、圣保罗研究基金会（FAPESP）、教学研究与推广活动支持基金（FAEPEX）以及高等教育人才发展协调局（CAPES）的项目资助。 免责声明：本文本中表达的观点、假设、结论或建议仅代表作者本人，并不必然代表桑坦德银行、CNPq、FAPESP、FAEPEX或CAPES的官方立场。 参考文献 [1] F. C. Pereira、P. J. de Rezende 和 T. Yunes. 面向社交网络影响力传播的混合数学启发式算法. 2024, 已投稿. [2] S. Raghavan 和 R. Zhang. 社交网络加权目标集选择问题的分支割解法. 2024. https://doi.org/10.1287/ijoo.2019.0012 [3] J. Leskovec 和 A. Krevl. SNAP数据集：斯坦福大型网络数据集集. 2024. https://snap.stanford.edu/data [4] R. A. Rossi 和 N. K. Ahmed. 支持交互式图分析与可视化的网络数据仓库. 2022. https://networkrepository.com [5] J. Kunegis. KONECT——科布伦茨网络数据集集. 2013. http://dl.acm.org/citation.cfm?id=2488173 [6] O. Lesser、L. Tenenboim-Chekina、L. Rokach 和 Y. Elovici. 入侵者还是好友：在线社交网络中的群体成员身份推断. 2013. https://doi.org/10.1007/978-3-642-37210-0_40
