Publications by authors named "Wu-Dong Xi"

Community detection has become a prominent task in complex network analysis. However, most of the existing methods for community detection only focus on the lower order structure at the level of individual nodes and edges and ignore the higher order connectivity patterns that characterize the fundamental building blocks within the network. In recent years, researchers have shown interest in motifs and their role in network analysis.

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To alleviate the sparsity issue, many recommender systems have been proposed to consider the review text as the auxiliary information to improve the recommendation quality. Despite success, they only use the ratings as the ground truth for error backpropagation. However, the rating information can only indicate the users' overall preference for the items, while the review text contains rich information about the users' preferences and the attributes of the items.

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Synopsis of recent research by authors named "Wu-Dong Xi"

  • - Wu-Dong Xi's research primarily focuses on enhancing complex network analysis techniques, particularly in community detection, through the exploration of higher order connectivity patterns and motifs that better represent network structures.
  • - His work on motif-based contrastive learning aims to improve the effectiveness of community detection methods, which traditionally overlook these higher order patterns, thus providing a more nuanced understanding of network dynamics.
  • - In the domain of recommendation systems, Xi has developed a neural network approach that integrates item ratings and review texts, addressing limitations posed by sparsity in recommendation data and enhancing the quality of item recommendations through richer user preference insights.