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A local density optimization method based on a graph convolutional network

Frontiers of Information Technology & Electronic Engineering 2020年第12期21卷 1795-1803页

作者： Hao WANG Li-yan DONG Tie-hu FAN Ming-hui SUN College of Computer Science and Technology Jilin UniversityChangchun 130012China MOE Key Laboratory of Symbolic Computation and Knowledge Engineering Jilin UniversityChangchun 130012China College of Instrumentation&Electrical Engineering Jilin UniversityChangchun 130012China

Success has been obtained using a semi-supervised graph analysis method based on a graph convolutional network(GCN).However,GCN ignores some local information at each node in the graph,so that data preprocessing is incomplete and the model generated is not accurate enough.Thus,in the case of numerous unsupervised models based on graph embedding technology,local node information is important.In this paper,we apply a local analysis method based on the similar neighbor hypothesis to a GCN,and propose a local density definition;we call this method LDGCN.The LDGCN algorithm processes the input data of GCN in two methods,i.e.,the unbalanced and balanced methods.Thus,the optimized input data contains detailed local node information,and then the model generated is accurate after training.We also introduce the implementation of the LDGCN algorithm through the principle of GCN,and use three mainstream datasets to verify the effectiveness of the LDGCN algorithm(i.e.,the Cora,Citeseer,and Pubmed datasets).Finally,we compare the performances of several mainstream graph analysis algorithms with that of the LDGCN algorithm.Experimental results show that the LDGCN algorithm has better performance in node classification tasks.

关键词： Semi-supervised learning Graph convolutional network Graph embedding local density

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Pointwise manifold regularization for semi-supervised learning

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Frontiers of Computer Science 2021年第1期15卷 91-98页

作者： Yunyun WANG Jiao HAN Yating SHEN Hui XUE Department of Computer Science and Engineering Nanjing University of Posts&TelecommunicationsNanjing 210046China School of Computer Science and Engineering Southeast UniversityNanjing 210096China

Manifold regularization(MR)provides a powerful framework for semi-supervised classification using both the labeled and unlabeled data.It constrains that similar instances over the manifold graph should share similar classification out-puts according to the manifold assumption.It is easily noted that MR is built on the pairwise smoothness over the manifold graph,i.e.,the smoothness constraint is implemented over all instance pairs and actually considers each instance pair as a single operand.However,the smoothness can be pointwise in nature,that is,the smoothness shall inherently occur“everywhere"to relate the behavior of each point or instance to that of its close neighbors.Thus in this paper,we attempt to de-velop a pointwise MR(PW_MR for short)for semi-supervised learning through constraining on individual local instances.In this way,the pointwise nature of smoothness is preserved,and moreover,by considering individual instances rather than instance pairs,the importance or contribution of individual instances can be introduced.Such importance can be described by the confidence for correct prediction,or the local density,for example.PW.MR provides a different way for implementing manifold smoothness Finally,empirical results show the competitiveness of PW_MR compared to pairwise MR.

关键词： semi-supervised classification manifold regularization pairwise smoothness pointwise smoothness local density

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A Hypergraph Model for Clustering Scale-free Network

A Hypergraph Model for Clustering Scale-free Network

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第二十七届中国控制会议

作者： Zheng Yu 1,Qian Rong 2 1.College of Science,Northeast Forestry University,Harbin 150040,P.R.china 2.Department of Computer Science,Beijing Electronic Science and Technology Institute,Beijing 100070,P.R.china

Many complex networks possess the scale-free property,which makes the task of detecting communities from these networks difficult.The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper we present a method of detecting community structure based on hypergraph model to address this problem.The hypergraph model maps the relationship in the original data into a hypergraph.A hyperedge represents a relationship among subsets of data and the weight of the hyperedge reflects the strength of this affinity.We assign the density of a hyperedge to its weight.We present and illustrate the results of experiments on the Enron data set.The experiments demonstrate that our approach is applicable and effective.

关键词： Scale-free Hypergraph model Community structure local density

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