In the landscape of spatio-temporal data analytics effective trajectory representation learning
is paramount. To bridge the gap of learning accurate representations with efficient and …

Network embedding has shown promising performance in real-world applications. The
network embedding typically lies in a continuous vector space, where storage and …

Graphs provide a natural, intuitive, and holistic means to capture relationships between
different text elements in Natural Language Processing (NLP) such as words, sentences …

Differential privacy is commonly used for graph analysis in the interactive setting, were a
query of some graph statistic is answered with additional noise to avoid leaking private …

Updating a truncated Singular Value Decomposition (SVD) is crucial in representation
learning, especially when dealing with large-scale data matrices that continuously evolve in …

Applying the Skip-gram to graph representation learning has become a widely researched
topic in recent years. Prior works usually focus on the migration application of the Skip-gram …

Recent advancement of Knowledge distillation (KD) is to extract and transfer middle layer
knowledge of teacher models to student models, which is better than original KDs which …

Differential privacy is commonly used for graph analysis in the interactive setting, were a
query of some graph statistic is answered with additional noise to avoid leaking private …