We propose DyGFormer, a new Transformer-based architecture for dynamic graph learning.
DyGFormer is conceptually simple and only needs to learn from nodes' historical first-hop …

Sea surface temperature (SST) is one critical parameter of global climate change, and
accurate SST prediction is important to various applications, eg, weather forecasting, fishing …

Many graph representation learning (GRL) problems are dynamic, with millions of edges
added or removed per second. A fundamental workload in this setting is dynamic link …

Given a sequence of sets, where each set contains an arbitrary number of elements,
temporal sets prediction aims to predict which elements will appear in the subsequent set …

Knowledge Tracing aims to assess student learning states by predicting their performance in
answering questions. Different from the existing research which utilizes fixed-length learning …

Accurate prediction of what types of patents that companies will apply for in the next period
of time can figure out their development strategies and help them discover potential partners …

Structure encoding has proven to be the key feature to distinguishing links in a graph.
However, Structure encoding in the temporal graph keeps changing as the graph evolves …

Given a sequence of sets, where each set has a timestamp and contains an arbitrary
number of elements, temporal sets prediction aims to predict the elements in the subsequent …

Next set recommendation aims to predict the items that are likely to be bought in the next
purchase. Central to this endeavor is the task of capturing intra-set and cross-set …

Next Set Recommendation (NSRec), encompassing related tasks such as next basket
recommendation and temporal sets prediction, stands as a trending research topic. Although …