Xiangguo Sun

TL;DR: A novel framework that considers multi-level graph convolutions on both local network structure and hypergraph structure in a unified manner is proposed that overcomes data insufficiency problem of existing work and does not necessarily rely on user demographic information.

TL;DR: This paper proposes a model named 2CLSTM, which is a bidirectional LSTMs (Long Short Term Memory networks) concatenated with CNN (Convolutional Neural Network), to detect user's personality using structures of texts to show that the structure of texts can be also an important feature in the study of personality detection from texts.

TL;DR: TMER as discussed by the authors utilizes well-designed item-item path modeling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable recommendations.

TL;DR: This work proposes a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations and shows that relationships beyond pairwise are also advantageous in the spammer detection.

TL;DR: Guo et al. as discussed by the authors proposed a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations.