Titlebook: Database Systems for Advanced Applications; 29th International C Makoto Onizuka,Jae-Gil Lee,Kejing Lu Conference proceedings 2024 The Edito

性上癮

STELL

Social Relation Enhanced Heterogeneous Graph Contrastive Learning for?Recommendationsers’ interests. These systems have showcased their significance in diverse scenarios, with particular prominence observed in applications related to social networks. Heterogeneous Graph Neural Networks (HGNNs) have shown success in recommendation tasks by embedding rich semantics from different rel

Missile

Higher-Order Graph Contrastive Learning for?Recommendation-item). However, the graph-based model struggles to mitigate the impact of data sparsity. Recent studies have attempted to tackle this problem by utilizing contrastive learning. Nevertheless, most of these methods rely on augmenting the data based on the original graph to construct contrastive views

endure

: Evaluating the?Importance of?Propagations during Fake News Spread content, which may fail to determine fake news with disguised content. Graph-based models adopt extra media to construct graphs, which provide social context to identify fake news. However, existing graph-based models treat each media equally, neglecting the echo chamber phenomenon where most media

output

健壯

placebo-effect

表皮

Beyond the?Known: Novel Class Discovery for?Open-World Graph Learningnarios, novel classes can emerge?on unlabeled testing nodes. However, little attention has been paid?to novel class discovery on graphs. Discovering novel classes is challenging as novel and known class nodes are correlated?by edges, which makes their representations indistinguishable?when applying

昆蟲

針葉