A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and oth...A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.展开更多
Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the stru...Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE.展开更多
针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户...针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户和物品邻居节点补充元路径缺失的信息,以卷积的方式捕获节点之间丰富的交互,通过注意力机制得到节点和元路径的嵌入表示,拼接用户、物品、邻居节点及元路径进行TOP-N推荐。在两个公开数据集上的实验结果表明,NMRec推荐性能良好,对推荐结果有良好的可解释性,与7种推荐基准算法相比,NMRec在评价指标Pre@10、Recall@10、NDGG@10上至少提升了0.21%、29%、1.46%。展开更多
异构信息网络(Heterogeneous Information Network, HIN)凭借其丰富的语义信息和结构信息被广泛应用于推荐系统中,虽然取得了很好的推荐效果,但较少考虑局部特征放大、信息交互和多嵌入聚合等问题。针对这些问题,提出了一种新的用于top-...异构信息网络(Heterogeneous Information Network, HIN)凭借其丰富的语义信息和结构信息被广泛应用于推荐系统中,虽然取得了很好的推荐效果,但较少考虑局部特征放大、信息交互和多嵌入聚合等问题。针对这些问题,提出了一种新的用于top-N推荐的多嵌入融合推荐(Multi-embedding Fusion Recommendation, MFRec)模型。首先,该模型在用户和项目学习分支中都采用对象上下文表示网络,充分利用上下文信息以放大局部特征,增强相邻节点的交互性;其次,将空洞卷积和空间金字塔池化引入元路径学习分支,以便获取多尺度信息并增强元路径的节点表示;然后,采用多嵌入融合模块以便更好地进行用户、项目以及元路径的嵌入融合,细粒度地进行多嵌入之间的交互学习,并强调了各特征的不同重要性程度;最后,在两个公共推荐系统数据集上进行了实验,结果表明所提模型MFRec优于现有的其他top-N推荐系统模型。展开更多
基金Science and Technology Research Project of Jiangxi Provincial Department of Education(Project No.GJJ211348,GJJ211347 and GJJ2201056)。
文摘A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.
基金supported by the National Key Research and Development Plan of China(2017YFB0503700,2016YFB0501801)the National Natural Science Foundation of China(61170026,62173157)+1 种基金the Thirteen Five-Year Research Planning Project of National Language Committee(No.YB135-149)the Fundamental Research Funds for the Central Universities(Nos.CCNU20QN022,CCNU20QN021,CCNU20ZT012).
文摘Real-world complex networks are inherently heterogeneous;they have different types of nodes,attributes,and relationships.In recent years,various methods have been proposed to automatically learn how to encode the structural and semantic information contained in heterogeneous information networks(HINs)into low-dimensional embeddings;this task is called heterogeneous network embedding(HNE).Efficient HNE techniques can benefit various HIN-based machine learning tasks such as node classification,recommender systems,and information retrieval.Here,we provide a comprehensive survey of key advancements in the area of HNE.First,we define an encoder-decoder-based HNE model taxonomy.Then,we systematically overview,compare,and summarize various state-of-the-art HNE models and analyze the advantages and disadvantages of various model categories to identify more potentially competitive HNE frameworks.We also summarize the application fields,benchmark datasets,open source tools,andperformance evaluation in theHNEarea.Finally,wediscuss open issues and suggest promising future directions.We anticipate that this survey will provide deep insights into research in the field of HNE.
文摘针对现有模型对异质信息网络(heterogeneous information network, HIN)信息提取大部分依赖于元路径,缺乏元路径信息补充以及很少学习异质图中复杂的结构信息等问题,提出一种异质网中基于邻居节点和元路径的推荐算法(NMRec)。提取用户和物品邻居节点补充元路径缺失的信息,以卷积的方式捕获节点之间丰富的交互,通过注意力机制得到节点和元路径的嵌入表示,拼接用户、物品、邻居节点及元路径进行TOP-N推荐。在两个公开数据集上的实验结果表明,NMRec推荐性能良好,对推荐结果有良好的可解释性,与7种推荐基准算法相比,NMRec在评价指标Pre@10、Recall@10、NDGG@10上至少提升了0.21%、29%、1.46%。
