Personalized Learning Task Assignment Based on Bipartite Graph by TAN Zhen-qiong, JIANG Wen-Jun, YUM Yen-na-cherry, ZHANG Ji, YUM Peter-tak-shing, LI Xiao-hong

Subjects: “…bipartite graph|task allocation|time factor|learning effect|transfer learning…”
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具有较小秩的带号图的刻画(A characterization of signed graphs with small rank) by SULi(苏莉), LIHong-hai(李红海), CHENChun-fang(陈春芳)

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Method for Abnormal Users Detection Oriented to E-commerce Network by DU Hang-yuan, LI Duo, WANG Wen-jian

“…In the e-commerce network,abnormal users often show different behavioral characteristics from normal users.Detecting abnormal users and analyzing their behavior patterns is of great practical significance to maintaining the order of e-commerce platforms.By analyzing the behavior patterns of abnormal users,we abstract the e-commerce network into the heterogeneous information network,and convert it into a user-device bipartite graph.On this basis,we propose a method for detecting abnormal users oriented to e-commerce network——self-supervised anomaly detection model(S-SADM).The model has a self-supervised learning mechanism.It uses an autoencoder to encode the user-device bipartite graph to obtain user node representations.By optimizing the joint objective function,the model completes backpropagation,and uses support vector data descriptions to perform anomaly detection on user node representations.After the automatic iterative optimization of the network,the user node representation has supervised information,and we obtain relatively stable detection results.Finally,S-SADM is validated on 3 real network datasets and a semi-synthetic network dataset,and the experimental results demonstrate the effectiveness and superiority of the method.…”
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Deep Disentangled Collaborative Filtering with Graph Global Information by HAO Jingyu, WEN Jingxuan, LIU Huafeng, JING Liping, YU Jian

“…GCN-based collaborative filtering models generate the representation of user nodes and item nodes by aggregating information on user-item interaction bipartite graph,and then predict users' preferences on items.However,they neglect users' different interaction intents and cannot fully explore the relationship between users and items.Existing graph disentangled collaborative filtering models model users' interaction intents,but ignore the global information of interaction graph and the essential features of users and items,causing the incompleteness of representation semantics.Furthermore,disentangled representation learning is inefficient due to the iterative structure of model.To solve these problems,this paper devises a deep disentangled collaborative filtering model incorporating graph global information,which is named as global graph disentangled collaborative filtering(G2DCF).G2DCF builds graph global channel and graph disentangled channel,which learns essential features and intent features,respectively.Meanwhile,by introducing orthogonality constraint and representation independence constraint,G2DCF makes every user-item interaction intent as unique as possible to prevent intent degradation,and raises the independence of representations under different intents,so as to improve the disentanglement effect.Compared with the previous graph collaborative filtering models,G2DCF can more comprehensively describe features of users and items.A number of experiments are conducted on three public datasets,and results show that the proposed method outperforms the comparison methods on multiple metrics.Further,this paper analyzes the representation distributions from independence and uniformity,verifies the disentanglement effect.It also compares the convergence speed to verify the effectiveness.…”
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