基于Transformer的节点影响力排序模型

Node Influence Ranking Model Based on Transformer

节点影响力排序是复杂网络的一个重点话题,对识别关键节点和衡量节点影响力有着重要作用。目前,已有诸多研究基于复杂网络探索节点影响力,其中深度学习显示出了巨大的潜力。然而,现有卷积神经网络(CNNs)和图神经网络(GNNs)模型的输入往往基于固定维度特征,且不能有效地区分邻居节点,无法适应多样性的复杂网络。为了解决上述问题,文中提出了一种简单且有效的节点影响力排序模型。该模型中,节点的输入序列包含节点本身及其邻居节点的信息,且可以根据网络动态调整输入序列长度,确保模型获取到足量的节点信息。同时该模型利用自注意力机制,使节点可以有效地聚合输入序列中邻居节点的信息,从而全面地识别节点的影响力。在12个真实网络数据集上进行实验,通过多维度的评价标准验证了该模型相比7种已有方法的有效性。实验结果表明,在不同的网络结构中,该模型均能有效地识别网络中节点的影响力。

Node influence ranking is a key topic in complex networks,and plays an important role in identifying key nodes and measuring node influence.There has been much research exploring node influence based on complex networks,with deep learning shows great potential.However,existing convolutional neural networks(CNNs)and graph neural networks(GNNs)are often based on fixed dimensional features as input and cannot effectively distinguish between neighboring nodes,making them unsuitable for diverse complex networks.In order to solve these problems,a simple and effective node influence ranking model is proposed in this paper.In this model,the input sequence of nodes contains information about the nodes themselves and their neighbors,and the length of the input sequence can be dynamically adjusted according to the network to ensure that the model obtains sufficient information about the nodes.The model also uses the self-attention mechanism to enable nodes to efficiently aggregate information about their neighbors in the input sequence,thus identifying the influence of nodes.Experiments are conducted on 12 real network datasets to verify the effectiveness of the model against seven existing methods using multi-dimensional evaluation criteria.Experimental results show that the model can identify the influence of nodes in complex networks more effectively.