[HTML][HTML] Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review
I Malashin, V Tynchenko, A Gantimurov, V Nelyub… - …, 2024 - pmc.ncbi.nlm.nih.gov
This review explores the application of Long Short-Term Memory (LSTM) networks, a
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
specialized type of recurrent neural network (RNN), in the field of polymeric sciences. LSTM …
A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges
Dynamic Graph Neural Networks (GNNs) combine temporal information with GNNs to
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …
capture structural, temporal, and contextual relationships in dynamic graphs simultaneously …
Out-of-distribution generalized dynamic graph neural network with disentangled intervention and invariance promotion
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to …
SCFL: Spatio-temporal consistency federated learning for next POI recommendation
Existing personalized federated learning frameworks fail to significantly improve the
personalization of user preference learning in next Point-Of-Interest (POI) recommendations …
personalization of user preference learning in next Point-Of-Interest (POI) recommendations …
Global and local hypergraph learning method with semantic enhancement for POI recommendation
The deep semantic information mining extracts deep semantic features from textual data and
effectively utilizes the world knowledge embedded in these features, so it is widely …
effectively utilizes the world knowledge embedded in these features, so it is widely …
Disentangled Dynamic Graph Attention Network for Out-of-Distribution Sequential Recommendation
Sequential recommendation, leveraging user-item interaction histories to provide
personalized and timely suggestions, has drawn significant research interest recently. With …
personalized and timely suggestions, has drawn significant research interest recently. With …
SFTe: Temporal knowledge graphs embedding for future interaction prediction
W Jia, R Ma, W Niu, L Yan, Z Ma - Information Systems, 2024 - Elsevier
Interaction prediction is a crucial task in the Social Internet of Things (SIoT), serving diverse
applications including social network analysis and recommendation systems. However, the …
applications including social network analysis and recommendation systems. However, the …
Toward Structure Fairness in Dynamic Graph Embedding: A Trend-aware Dual Debiasing Approach
Recent studies successfully learned static graph embeddings that are structurally fair by
preventing the effectiveness disparity of high-and low-degree vertex groups in downstream …
preventing the effectiveness disparity of high-and low-degree vertex groups in downstream …
TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation
Dynamic recommendation systems, where users interact with items continuously over time,
have been widely deployed in real-world online streaming applications. The burst of …
have been widely deployed in real-world online streaming applications. The burst of …
DTGB: A Comprehensive Benchmark for Dynamic Text-Attributed Graphs
Dynamic text-attributed graphs (DyTAGs) are prevalent in various real-world scenarios,
where each node and edge are associated with text descriptions, and both the graph …
where each node and edge are associated with text descriptions, and both the graph …