在社交网络中最大限度地影响使用结合本地特征和基于深度学习的节点嵌入
Asgarali Bouyer1,2, Hamid Ahmadi Beni3, Amin Golzari Oskouei2
1Department of Software Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.
Big data
|October 22, 2024
概括
本研究介绍了影响力最大化嵌入技术 (ETIM),这是一种新的算法,可以显著提高感染率并减少大规模网络中的计算时间. 通过图形嵌入和局部结构特征,ETIM有效地识别了有影响力的节点.
科学领域:
- 网络科学 网络科学
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
背景情况:
- 影响力最大化对于病毒营销和信息传播至关重要,但在大型网络中面临着低感染率和高时间复杂性等挑战.
- 现有的方法通常由于计算需求或依赖自由参数而难以扩展,从而限制了它们的实际应用.
- 解决这些局限性对于在复杂的现实世界网络中开发高效的影响力最大化策略至关重要.
研究的目的:
- 提出一种新的局部启发式算法,即影响最大化嵌入技术 (ETIM),旨在克服现有的影响最大化方法的局限性.
- 通过减少搜索空间和计算开销,提高大规模网络影响力最大化的效率和有效性.
- 与现有的算法相比,提高感染率和解决方案质量.
主要方法:
- ETIM采用外分解,图形嵌入和缩小,整合了候选节点选择的本地结构特征.
- 基于深度学习的节点嵌入技术为候选节点生成多维向量,捕捉复杂的网络关系.
- 节点对扩散的依赖性是使用本地拓特征计算的,其次是根据结合的本地和嵌入式特征识别有影响力的节点.
主要成果:
- 当使用独立级联模型进行评估时,ETIM表现出竞争力,并在解决方案质量方面取得了卓越的表现.
- 该算法通过专注于网络外和拓特征,显著降低了计算开销和搜索空间.
- ETIM在感染率上取得了实质性的改善,平均比集体影响全球算法高出12%,同时速度更快.
结论:
- 影响力最大化嵌入技术 (ETIM) 为大规模网络中的影响力最大化问题提供了高效和有效的解决方案.
- 通过将图形嵌入与局部结构分析相结合的混合方法,ETIM成功地解决了时间复杂性和解决方案质量的挑战.
- 拟议的方法为需要在复杂网络结构中有效识别有影响力的节点的应用提供了有希望的进步.
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