随着在线社交应用和媒体的迅速扩散,在线社交网络(Online Social Network,OSN)已将我们的日常生活与网络信息空间连接起来.这些连接产生了大量的数据,不仅包括传播信息,还包括用户行为.社交关系挖掘的研究是社交网络挖掘中的一个重要领...随着在线社交应用和媒体的迅速扩散,在线社交网络(Online Social Network,OSN)已将我们的日常生活与网络信息空间连接起来.这些连接产生了大量的数据,不仅包括传播信息,还包括用户行为.社交关系挖掘的研究是社交网络挖掘中的一个重要领域,为我们对网络的形成机理、用户的交互模式和动态机制的理解提供了一个机会.社交关系(Social Ties)是社交网络中人与人连接和交互的纽带,也是社交网络中信息传播的基础.从计算学的观点来看,社交关系挖掘的研究包括社交关系的形成机理、社交关系的语义化以及基于社交关系人与人之间的交互.该文综述性地分析了这3个方面的研究现状,具体来说,在社交关系形成机理方面介绍关系链接预测,在基于社交关系的交互方面介绍关系交互预测,在社交关系语义化方面介绍关系类型预测.首先给出社交网络分析问题的形式化描述和相关概念、常用数据,然后分别介绍关系链接预测、关系类型预测和关系交互预测3个方面的方法、理论和模型,并给出重要的应用实例及其效果.最后,该文给出了未来工作的展望.展开更多
Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods ...Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods for drug-target interaction(DTI)identification remain either time consuming or heavily dependent on domain expertise.Therefore,various computational models have been proposed to predict possible interactions between drugs and target proteins.However,most prediction methods do not consider the topological structures characteristics of the relationship.In this paper,we propose a relational topologybased heterogeneous network embedding method to predict drug-target interactions,abbreviated as RTHNE_DTI.We first construct a heterogeneous information network based on the interaction between different types of nodes,to enhance the ability of association discovery by fully considering the topology of the network.Then drug and target protein nodes can be represented by the other types of nodes.According to the different topological structure of the relationship between the nodes,we divide the relationship in the heterogeneous network into two categories and model them separately.Extensive experiments on the realworld drug datasets,RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods.RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.展开更多
The lost information caused by feature interaction is restored by using auxiliary faces (AF) and virtual links (VL). The delta volume of the interacted features represented by concave attachable connected graph (CACG)...The lost information caused by feature interaction is restored by using auxiliary faces (AF) and virtual links (VL). The delta volume of the interacted features represented by concave attachable connected graph (CACG) can be decomposed into several isolated features represented by complete concave adjacency graph (CCAG). We can recognize the feature’s sketchy type by using CCAG as a hint; the exact type of the feature can be attained by deleting the auxiliary faces from the isolated feature. United machining feature (UMF) is used to represent the features that can be machined in the same machining process. It is important to the rationalizing of the process plans and reduce the time costing in machining. An example is given to demonstrate the effectiveness of this method.展开更多
Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used b...Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.展开更多
文摘随着在线社交应用和媒体的迅速扩散,在线社交网络(Online Social Network,OSN)已将我们的日常生活与网络信息空间连接起来.这些连接产生了大量的数据,不仅包括传播信息,还包括用户行为.社交关系挖掘的研究是社交网络挖掘中的一个重要领域,为我们对网络的形成机理、用户的交互模式和动态机制的理解提供了一个机会.社交关系(Social Ties)是社交网络中人与人连接和交互的纽带,也是社交网络中信息传播的基础.从计算学的观点来看,社交关系挖掘的研究包括社交关系的形成机理、社交关系的语义化以及基于社交关系人与人之间的交互.该文综述性地分析了这3个方面的研究现状,具体来说,在社交关系形成机理方面介绍关系链接预测,在基于社交关系的交互方面介绍关系交互预测,在社交关系语义化方面介绍关系类型预测.首先给出社交网络分析问题的形式化描述和相关概念、常用数据,然后分别介绍关系链接预测、关系类型预测和关系交互预测3个方面的方法、理论和模型,并给出重要的应用实例及其效果.最后,该文给出了未来工作的展望.
基金funded by the National Natural Science Foundation of China,grant number 61402220the key program of Scientific Research Fund of Hunan Provincial Education Department,grant number 19A439the Project supported by the Natural Science Foundation of Hunan Province,China,grant number 2020J4525 and grant number 2022J30495.
文摘Predicting interactions between drugs and target proteins has become an essential task in the drug discovery process.Although the method of validation via wet-lab experiments has become available,experimental methods for drug-target interaction(DTI)identification remain either time consuming or heavily dependent on domain expertise.Therefore,various computational models have been proposed to predict possible interactions between drugs and target proteins.However,most prediction methods do not consider the topological structures characteristics of the relationship.In this paper,we propose a relational topologybased heterogeneous network embedding method to predict drug-target interactions,abbreviated as RTHNE_DTI.We first construct a heterogeneous information network based on the interaction between different types of nodes,to enhance the ability of association discovery by fully considering the topology of the network.Then drug and target protein nodes can be represented by the other types of nodes.According to the different topological structure of the relationship between the nodes,we divide the relationship in the heterogeneous network into two categories and model them separately.Extensive experiments on the realworld drug datasets,RTHNE_DTI produces high efficiency and outperforms other state-of-the-art methods.RTHNE_DTI can be further used to predict the interaction between unknown interaction drug-target pairs.
文摘The lost information caused by feature interaction is restored by using auxiliary faces (AF) and virtual links (VL). The delta volume of the interacted features represented by concave attachable connected graph (CACG) can be decomposed into several isolated features represented by complete concave adjacency graph (CCAG). We can recognize the feature’s sketchy type by using CCAG as a hint; the exact type of the feature can be attained by deleting the auxiliary faces from the isolated feature. United machining feature (UMF) is used to represent the features that can be machined in the same machining process. It is important to the rationalizing of the process plans and reduce the time costing in machining. An example is given to demonstrate the effectiveness of this method.
基金This work is supported by the National Basic Research 973 Program of China under Grant Nos. 2013CB329602 and 2014CB340405, the National Natural Science Foundation of China under Grant Nos. 61173008, 61232010, 60933005, 61402442, 61402022, and 61303244, Beijing Nova Program under Grant No. Z121101002512063, and the Natural Science Foundation of Beijing under Grant No. 4154086.
文摘Link prediction in microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F 1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweeting similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real-world Twitter data show that our model outperforms state-of-the-art methods.
