In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentba...In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.展开更多
This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability c...This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability criteria for heterogeneous networks with multi-relational links beyond those only applicable to networks with single-relational links.It is found a network with multi-relational links can be controllable even if each single-relational network topology is uncontrollable,and vice versa.Some sufficient and necessary conditions are derived for the controllability of multi-relational networks with heterogeneous dynamical nodes.For two typical multi-relational networks with star-chain topology and star-circle topology,some easily verified conditions are presented.For illustration and verification,several examples are presented.These findings provide practical insights for the analysis and control of multi-relational complex systems.展开更多
In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as...In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.展开更多
At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the tradit...At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the traditional research method of internet public opinion pre-waming mainly relies on manual analysis, which is too inefficient to adapt to the analysis of massive internet public opinion information. According to the above analysis, this paper puts forward an internet public opinion pre-warning mechanism on emergent event based on multi-relational data clustering algorithm, discusses the specific pre-waming from the aspects of the state and dissemination of internet public opinions and the historical data, and automatically classifies the internet public opinions through multi-relational data clustering algorithm. And the results show that such method can be used to effectively study the internet public opinion pre-waming on emergent event, with the accuracy rate of as high as 95%.展开更多
Financial statement fraud refers to malicious manipulations of financial data in listed companies'annual statements.Traditional machine learning approaches focus on individual companies,overlooking the interactive...Financial statement fraud refers to malicious manipulations of financial data in listed companies'annual statements.Traditional machine learning approaches focus on individual companies,overlooking the interactive relationships among companies that are crucial for identifying fraud patterns.Moreover,fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud ones.In this paper,we propose a multi-relational graph convolutional network,named FraudGCN,for detecting financial statement fraud.A multi-relational graph is constructed to integrate industrial,supply chain,and accounting-sharing relationships,effectively encapsulating the multidimensional and complex interactions among companies.We then develop a multi-relational graph convolutional network to aggregate information within each relationship and employ an attention mechanism to fuse information across multiple relationships.The attention mechanism enables the model to distinguish the importance of different relationships,thereby aggregating more useful information from key relationships.To alleviate the class imbalance problem,we present a diffusion-based under-sampling strategy that strategically selects key nodes globally for model training.We also employ focal loss to assign greater weights to harder-to-classify minority samples.We build a real-world dataset from the annual financial statement of listed companies in China.The experimental results show that FraudGCN achieves an improvement of 3.15%in Macro-recall,3.36%in Macro-F1,and 3.86%in GMean compared to the second-best method.The dataset and codes are publicly available at:https://github.com/XNetLab/MRG-for-Finance.展开更多
Knowledge Graph Completion(KGC)aims to predict missing links in a knowledge graph.A popular model for this task is the Graph Neural Network(GNN),which leverages structural information from neighboring nodes.However,cu...Knowledge Graph Completion(KGC)aims to predict missing links in a knowledge graph.A popular model for this task is the Graph Neural Network(GNN),which leverages structural information from neighboring nodes.However,current GNN-based methods treat all neighbors equally,overlooking the importance of entity neighbors and handling complex relationships effectively.To address these challenges,we introduce the Hierarchical Entity Neighbor Multi-Relational Fusion Network(HENF)for KGC.HENF offers fine-grained adaptability to various multi-relational scenarios.It constructs relationship subgraphs based on one-hop paths between entities,aggregating information around entities using dynamic attention mechanisms.Furthermore,it employs Adjacent Relation Fusion(ARF)attention to combine rich entity information from different relational graphs.This approach allows our model to emphasize diverse semantic information types under various relations,selectively gather informative features,and assign appropriate weights.Extensive experiments demonstrate that HENF significantly enhances KGC performance,especially on datasets with many-to-many relationships.展开更多
Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.Design/m...Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.Design/methodology/approach: A heterogeneous researcher network has been constructed by combining multiple relations of academic researchers. Vertex attributes and their similarities were considered and calculated. An approach has been proposed and tested to detect research community in research organizations based on this multi-relation researcher network.Findings: Detection of topologically well-connected, semantically coherent and meaningful research community was achieved.Research limitations: The sample size of evaluation experiments was relatively small. In the present study, a limited number of 72 researchers were analyzed for constructing researcher network and detecting research community. Therefore, a large sample size is required to give more information and reliable results.Practical implications: The proposed multi-relation researcher network and approaches for discovering research communities of similar research interests will contribute to collective innovation behavior such as brainstorming and to promote interdisciplinary cooperation.Originality/value: Recent researches on community detection devote most efforts to singlerelation researcher networks and put the main focus on the topological structure of networks.In reality, there exist multi-relation social networks. Vertex attribute also plays an important role in community detection. The present study combined multiple single-relational researcher networks into a multi-relational network and proposed a structure-attribute clustering method for detecting research community in research organizations.展开更多
The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions....The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions. Hence, social network of academic researchers can be of important value for scientific community. This information can be retrieved from various data source currently available on the Web. From each of them a separate net-work can be built. In this paper we present a method which can be used to combine multiple single-relational networks into a single network which will combine all relations, hence it will be multi-relational.展开更多
The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transf...The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.展开更多
Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only rec...Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.展开更多
After a relation scheme R is decomposed into the set of schemes ρ={R_1,...,R_n},we may pose queries as if R existed in the database,taking a join of R_i's,when it is necessary to implement the query.Suppose a que...After a relation scheme R is decomposed into the set of schemes ρ={R_1,...,R_n},we may pose queries as if R existed in the database,taking a join of R_i's,when it is necessary to implement the query.Suppose a query involves a set of attributes S(?)R,we want to find the smallest subset of ρ whose union includes S.We prove that the problem is NP-complete and present a polynomial-bounded approximation algorithm.A subset of ρ whose union includes S and has a decomposition into 3NF with a lossless join and preservation of dependencies is given in the paper.展开更多
文摘In the era of exponential growth of digital information,recommender algorithms are vital for helping users navigate vast data to find relevant items.Traditional approaches such as collaborative filtering and contentbasedmethods have limitations in capturing complex,multi-faceted relationships in large-scale,sparse datasets.Recent advances in Graph Neural Networks(GNNs)have significantly improved recommendation performance by modeling high-order connection patterns within user-item interaction networks.However,existing GNN-based models like LightGCN and NGCF focus primarily on single-type interactions and often overlook diverse semantic relationships,leading to reduced recommendation diversity and limited generalization.To address these challenges,this paper proposes a dual multi-relational graph neural network recommendation algorithm based on relational interactions.Our approach constructs two complementary graph structures:a User-Item Interaction Graph(UIIG),which explicitly models direct user behaviors such as clicks and purchases,and a Relational Association Graph(RAG),which uncovers latent associations based on user similarities and item attributes.The proposed Dual Multi-relational Graph Neural Network(DMGNN)features two parallel branches that perform multi-layer graph convolutional operations,followed by an adaptive fusion mechanism to effectively integrate information from both graphs.This design enhances the model’s capacity to capture diverse relationship types and complex relational patterns.Extensive experiments conducted on benchmark datasets—including MovieLens-1M,Amazon-Electronics,and Yelp—demonstrate thatDMGNN outperforms state-of-the-art baselines,achieving improvements of up to 12.3%in Precision,9.7%in Recall,and 11.5%in F1 score.Moreover,DMGNN significantly boosts recommendation diversity by 15.2%,balancing accuracy with exploration.These results highlight the effectiveness of leveraging hierarchical multi-relational information,offering a promising solution to the challenges of data sparsity and relation heterogeneity in recommendation systems.Our work advances the theoretical understanding of multi-relational graph modeling and presents practical insights for developing more personalized,diverse,and robust recommender systems.
基金supported by the National Natural Science Foundation of China(61573077,U1808205)China Scholarship Council(202308130119)Natural Science Foundation of Hebei Province(F2022501005)。
文摘This paper studies the controllability of networked systems,in which the nodes are heterogeneous high-dimensional dynamical systems,and the links between nodes are multi-relational.Our aim is to find controllability criteria for heterogeneous networks with multi-relational links beyond those only applicable to networks with single-relational links.It is found a network with multi-relational links can be controllable even if each single-relational network topology is uncontrollable,and vice versa.Some sufficient and necessary conditions are derived for the controllability of multi-relational networks with heterogeneous dynamical nodes.For two typical multi-relational networks with star-chain topology and star-circle topology,some easily verified conditions are presented.For illustration and verification,several examples are presented.These findings provide practical insights for the analysis and control of multi-relational complex systems.
基金supported by National Natural Science Foundation of China under Grants No. 60933005, No. 91124002under Grants No. 012505, No. 2011AA010702, No. 2012AA01A401, No. 2012AA01A402 (863 program)+1 种基金under Grant No.2011A010 (242)NSTM under Grants No.2012BAH38B04, No.2012BAH38B06
文摘In micro-blogging contexts such as Twitter,the number of content producers can easily reach tens of thousands,and many users can participate in discussion of any given topic.While many users can introduce diversity,as not all users are equally influential,it makes it challenging to identify the true influencers,who are generally rated as being interesting and authoritative on a given topic.In this study,the influence of users is measured by performing random walks of the multi-relational data in micro-blogging:retweet,reply,reintroduce,and read.Due to the uncertainty of the reintroduce and read operations,a new method is proposed to determine the transition probabilities of uncertain relational networks.Moreover,we propose a method for performing the combined random walks for the multi-relational influence network,considering both the transition probabilities for intra-and inter-networking.Experiments were conducted on a real Twitter dataset containing about 260 000 users and 2.7million tweets,and the results show that our method is more effective than TwitterRank and other methods used to discover influencers.
文摘At present, there are some resistible illegal operations aiming at creating false public opinions in internet public opinions on emergent event, which seriously disrupted the normal Internet order. However, the traditional research method of internet public opinion pre-waming mainly relies on manual analysis, which is too inefficient to adapt to the analysis of massive internet public opinion information. According to the above analysis, this paper puts forward an internet public opinion pre-warning mechanism on emergent event based on multi-relational data clustering algorithm, discusses the specific pre-waming from the aspects of the state and dissemination of internet public opinions and the historical data, and automatically classifies the internet public opinions through multi-relational data clustering algorithm. And the results show that such method can be used to effectively study the internet public opinion pre-waming on emergent event, with the accuracy rate of as high as 95%.
基金supported by the National Natural Science Foundation of China(Nos.62272379,T2341003,U22B2019,and 62102310)the Natural Science Basic Research Plan in Shaanxi Province(No.2021JM-018)+1 种基金the Key R&D in Shaanxi Province(No.2023-YBGY-269)the Fundamental Research Funds for the Central Universities(No.xzy012023068).
文摘Financial statement fraud refers to malicious manipulations of financial data in listed companies'annual statements.Traditional machine learning approaches focus on individual companies,overlooking the interactive relationships among companies that are crucial for identifying fraud patterns.Moreover,fraud detection is a typical imbalanced binary classification task with normal samples outnumbering fraud ones.In this paper,we propose a multi-relational graph convolutional network,named FraudGCN,for detecting financial statement fraud.A multi-relational graph is constructed to integrate industrial,supply chain,and accounting-sharing relationships,effectively encapsulating the multidimensional and complex interactions among companies.We then develop a multi-relational graph convolutional network to aggregate information within each relationship and employ an attention mechanism to fuse information across multiple relationships.The attention mechanism enables the model to distinguish the importance of different relationships,thereby aggregating more useful information from key relationships.To alleviate the class imbalance problem,we present a diffusion-based under-sampling strategy that strategically selects key nodes globally for model training.We also employ focal loss to assign greater weights to harder-to-classify minority samples.We build a real-world dataset from the annual financial statement of listed companies in China.The experimental results show that FraudGCN achieves an improvement of 3.15%in Macro-recall,3.36%in Macro-F1,and 3.86%in GMean compared to the second-best method.The dataset and codes are publicly available at:https://github.com/XNetLab/MRG-for-Finance.
文摘Knowledge Graph Completion(KGC)aims to predict missing links in a knowledge graph.A popular model for this task is the Graph Neural Network(GNN),which leverages structural information from neighboring nodes.However,current GNN-based methods treat all neighbors equally,overlooking the importance of entity neighbors and handling complex relationships effectively.To address these challenges,we introduce the Hierarchical Entity Neighbor Multi-Relational Fusion Network(HENF)for KGC.HENF offers fine-grained adaptability to various multi-relational scenarios.It constructs relationship subgraphs based on one-hop paths between entities,aggregating information around entities using dynamic attention mechanisms.Furthermore,it employs Adjacent Relation Fusion(ARF)attention to combine rich entity information from different relational graphs.This approach allows our model to emphasize diverse semantic information types under various relations,selectively gather informative features,and assign appropriate weights.Extensive experiments demonstrate that HENF significantly enhances KGC performance,especially on datasets with many-to-many relationships.
基金supported by the National Natural Science Foundation of China(Grant No.:71203164)
文摘Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.Design/methodology/approach: A heterogeneous researcher network has been constructed by combining multiple relations of academic researchers. Vertex attributes and their similarities were considered and calculated. An approach has been proposed and tested to detect research community in research organizations based on this multi-relation researcher network.Findings: Detection of topologically well-connected, semantically coherent and meaningful research community was achieved.Research limitations: The sample size of evaluation experiments was relatively small. In the present study, a limited number of 72 researchers were analyzed for constructing researcher network and detecting research community. Therefore, a large sample size is required to give more information and reliable results.Practical implications: The proposed multi-relation researcher network and approaches for discovering research communities of similar research interests will contribute to collective innovation behavior such as brainstorming and to promote interdisciplinary cooperation.Originality/value: Recent researches on community detection devote most efforts to singlerelation researcher networks and put the main focus on the topological structure of networks.In reality, there exist multi-relation social networks. Vertex attribute also plays an important role in community detection. The present study combined multiple single-relational researcher networks into a multi-relational network and proposed a structure-attribute clustering method for detecting research community in research organizations.
文摘The majority of academic researchers present the results of their scientific activity on the Web. This trace can be used to derive useful information of their past, present activity and forecast the future intentions. Hence, social network of academic researchers can be of important value for scientific community. This information can be retrieved from various data source currently available on the Web. From each of them a separate net-work can be built. In this paper we present a method which can be used to combine multiple single-relational networks into a single network which will combine all relations, hence it will be multi-relational.
文摘The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.
基金the National Key Research and Development Program of China(Nos.2020YFC2003502,2021YFF0704101)the National Natural Science Foundation of China(Grant No.62276038)+1 种基金the Natural Science Foundation of Chongqing(Nos.cstc2019jcyj-cxttX0002,cstc2021ycjh-bgzxm0013)the Key Cooperation Project of Chongqing Municipal Education Commission(HZ20210-08).
文摘Uncertain Knowledge Graphs(UKGs)are used to characterize the inherent uncertainty of knowledge and have a richer semantic structure than deterministic knowledge graphs.The research on the embedding of UKG has only recently begun,Uncertain Knowledge Graph Embedding(UKGE)model has a certain effect on solving this problem.However,there are still unresolved issues.On the one hand,when reasoning the confidence of unseen relation facts,the introduced probabilistic soft logic cannot be used to combine multi-path and multi-step global information,leading to information loss.On the other hand,the existing UKG embedding model can only model symmetric relation facts,but the embedding problem of asymmetric relation facts has not be addressed.To address the above issues,a Multiplex Uncertain Knowledge Graph Embedding(MUKGE)model is proposed in this paper.First,to combine multiple information and achieve more accurate results in confidence reasoning,the Uncertain ResourceRank(URR)reasoning algorithm is introduced.Second,the asymmetry in the UKG is defined.To embed asymmetric relation facts of UKG,a multi-relation embedding model is proposed.Finally,experiments are carried out on different datasets via 4 tasks to verify the effectiveness of MUKGE.The results of experiments demonstrate that MUKGE can obtain better overall performance than the baselines,and it helps advance the research on UKG embedding.
文摘After a relation scheme R is decomposed into the set of schemes ρ={R_1,...,R_n},we may pose queries as if R existed in the database,taking a join of R_i's,when it is necessary to implement the query.Suppose a query involves a set of attributes S(?)R,we want to find the smallest subset of ρ whose union includes S.We prove that the problem is NP-complete and present a polynomial-bounded approximation algorithm.A subset of ρ whose union includes S and has a decomposition into 3NF with a lossless join and preservation of dependencies is given in the paper.
