本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integ...本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integrate the Node2vec and GCN methods. Initially, the Node2vec method is employed to obtain preliminary graph embeddings, which are then used as input to further update the graph embedding matrix through GCN. The study selects the Wikipedia dataset for node classification tasks, comparing the performance of the methods before and after integration to validate their effectiveness.展开更多
目前推荐系统普遍存在长尾问题,导致商品推荐覆盖率低、多样性差,为此提出一种融合有偏随机游走(Node2Vec)和负反馈强化学习的商品推荐算法GES4RL(Graph Embedding with Side Information for Reinforcement Learning)。对商品传播的有...目前推荐系统普遍存在长尾问题,导致商品推荐覆盖率低、多样性差,为此提出一种融合有偏随机游走(Node2Vec)和负反馈强化学习的商品推荐算法GES4RL(Graph Embedding with Side Information for Reinforcement Learning)。对商品传播的有向加权图使用Node2Vec算法学习商品的编码表示;引入门控循环单元(GRU)对用户偏好的动态情况进行建模,并使用基于负反馈强化学习模型计算出长尾商品的最佳推荐策略。在TianChi电商数据集上的实验表明,该算法显著提高了商品推荐的多样性和命中率。展开更多
通过对社会集群信息网络(Social Information Network,SIN)的分析,获取现实世界中特定目标的人际关系和社群结构,是社会集群信息网络研究的一个重要方向。这种研究在刑侦司法领域具有重大意义,能够使办案人员在不进行物理抓捕/监视的情...通过对社会集群信息网络(Social Information Network,SIN)的分析,获取现实世界中特定目标的人际关系和社群结构,是社会集群信息网络研究的一个重要方向。这种研究在刑侦司法领域具有重大意义,能够使办案人员在不进行物理抓捕/监视的情况下描绘出犯罪组织内部结构,进而找到犯罪组织核心成员。文章基于现有取证领域的相关研究,结合机器学习神经网络部分算法,提出一个网络取证工具Vec2Rank-CrimeNet,并以真实的犯罪数据作为实验数据,给出其在解决实际问题时的效果。展开更多
Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive atte...Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.展开更多
以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的...以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的分区号作为标签,通过深度信念神经网络进行训练,并通过训练后的模型对管网漏损位置进行检测。在实例分析中,以A市实际供水管网拓扑结构进行验证,利用MATLAB-Open Water Analytics toolbox联合编程建模,结果表明,各个时间段的检测效果均较优,正确率均达到为80%以上。因此,该模型能够有效地检测管网漏损。展开更多
文摘本文结合了Node2vec和GCN这两种方法,先利用Node2vec方法得到初步的图嵌入,之后将其作为输入利用GCN进一步更新图嵌入矩阵。本文选择在维基数据集上进行节点分类任务,比较了结合前后方法的表现,验证了其有效性。In this paper, we integrate the Node2vec and GCN methods. Initially, the Node2vec method is employed to obtain preliminary graph embeddings, which are then used as input to further update the graph embedding matrix through GCN. The study selects the Wikipedia dataset for node classification tasks, comparing the performance of the methods before and after integration to validate their effectiveness.
文摘目前推荐系统普遍存在长尾问题,导致商品推荐覆盖率低、多样性差,为此提出一种融合有偏随机游走(Node2Vec)和负反馈强化学习的商品推荐算法GES4RL(Graph Embedding with Side Information for Reinforcement Learning)。对商品传播的有向加权图使用Node2Vec算法学习商品的编码表示;引入门控循环单元(GRU)对用户偏好的动态情况进行建模,并使用基于负反馈强化学习模型计算出长尾商品的最佳推荐策略。在TianChi电商数据集上的实验表明,该算法显著提高了商品推荐的多样性和命中率。
文摘通过对社会集群信息网络(Social Information Network,SIN)的分析,获取现实世界中特定目标的人际关系和社群结构,是社会集群信息网络研究的一个重要方向。这种研究在刑侦司法领域具有重大意义,能够使办案人员在不进行物理抓捕/监视的情况下描绘出犯罪组织内部结构,进而找到犯罪组织核心成员。文章基于现有取证领域的相关研究,结合机器学习神经网络部分算法,提出一个网络取证工具Vec2Rank-CrimeNet,并以真实的犯罪数据作为实验数据,给出其在解决实际问题时的效果。
基金the National Natural Science Foundation of China(Nos.11861045 and 62162040)。
文摘Essential proteins are an indispensable part of cells and play an extremely significant role in genetic disease diagnosis and drug development.Therefore,the prediction of essential proteins has received extensive attention from researchers.Many centrality methods and machine learning algorithms have been proposed to predict essential proteins.Nevertheless,the topological characteristics learned by the centrality method are not comprehensive enough,resulting in low accuracy.In addition,machine learning algorithms need sufficient prior knowledge to select features,and the ability to solve imbalanced classification problems needs to be further strengthened.These two factors greatly affect the performance of predicting essential proteins.In this paper,we propose a deep learning framework based on temporal convolutional networks to predict essential proteins by integrating gene expression data and protein-protein interaction(PPI)network.We make use of the method of network embedding to automatically learn more abundant features of proteins in the PPI network.For gene expression data,we treat it as sequence data,and use temporal convolutional networks to extract sequence features.Finally,the two types of features are integrated and put into the multi-layer neural network to complete the final classification task.The performance of our method is evaluated by comparing with seven centrality methods,six machine learning algorithms,and two deep learning models.The results of the experiment show that our method is more effective than the comparison methods for predicting essential proteins.
文摘以深度学习框架为基础,提出了一种时空联合供水管网漏损检测模型。该模型首先运用Node2Vec算法求解不同时间段内节点特征;其次,通过模糊C-均值聚类法,利用管网模型节点特征进行分区。最后,以不同时间段的压力敏感度作为输入,漏损位置的分区号作为标签,通过深度信念神经网络进行训练,并通过训练后的模型对管网漏损位置进行检测。在实例分析中,以A市实际供水管网拓扑结构进行验证,利用MATLAB-Open Water Analytics toolbox联合编程建模,结果表明,各个时间段的检测效果均较优,正确率均达到为80%以上。因此,该模型能够有效地检测管网漏损。
