通过图神经网络发现动态网络中的结构洞跨越者
Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks
AI总结：
针对动态网络中结构洞跨越者发现这一NP难问题，提出基于GNN的GNN-SHS模型，以贪心方式识别top-k节点，在保持高精度的同时较对比方法最高平均加速671.6倍。
摘要：
结构洞（Structural Hole，SH）理论指出，在原本互不连通的社区之间充当连接纽带的节点会在网络中获得位置优势。这些节点被称为结构洞跨越者（Structural Hole Spanners，SHS）。SHS具有许多应用，包括病毒式营销、信息传播、社区检测等。已有大量解决方案被提出用于发现SHS；然而，大多数解决方案仅适用于静态网络。由于现实世界网络是动态网络，因此在本研究中，我们旨在发现动态网络中的SHS。发现SHS是一个NP难问题，因此我们不再发现精确的k个SHS，而是采用贪心方法来发现top-k SHS。受图神经网络（GNN）在各类图挖掘问题上成功的启发，我们设计了一个基于图神经网络的模型GNN-SHS，用于发现动态网络中的SHS，旨在降低计算成本同时实现高精度。我们通过详尽实验分析了所提模型的效率，结果表明，所提GNN-SHS模型比对比方法至少快31.8倍，平均快671.6倍，提供了显著的效率优势。
英文摘要：
Structural Hole (SH) theory states that the node which acts as a connecting link among otherwise disconnected communities gets positional advantages in the network. These nodes are called Structural Hole Spanners (SHS). SHSs have many applications, including viral marketing, information dissemination, community detection, etc. Numerous solutions are proposed to discover SHSs; however, most of the solutions are only applicable to static networks. Since real-world networks are dynamic networks; consequently, in this study, we aim to discover SHSs in dynamic networks. Discovering SHSs is an NP-hard problem, due to which, instead of discovering exact k SHSs, we adopt a greedy approach to discover top-k SHSs. Motivated from the success of Graph Neural Networks (GNNs) on various graph mining problems, we design a Graph Neural Network-based model, GNN-SHS, to discover SHSs in dynamic networks, aiming to reduce the computational cost while achieving high accuracy. We analyze the efficiency of the proposed model through exhaustive experiments, and our results show that the proposed GNN-SHS model is at least 31.8 times faster and, on an average 671.6 times faster than the comparative method, providing a considerable efficiency advantage.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
