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Neural Temporal Point Process for Forecasting Higher Order and Directional Interactions
Real-world systems are made of interacting entities that evolve with time.
Dynamic Representation Learning with Temporal Point Processes for Higher-Order Interaction Forecasting
As far as our knowledge, this is the first work that uses the temporal point process to forecast hyperedges in dynamic networks.
CoviHawkes: Temporal Point Process and Deep Learning based Covid-19 forecasting for India
Lockdowns are one of the most effective measures for containing the spread of a pandemic.
Adversarial Context Aware Network Embeddings for Textual Networks
In this paper we propose an approach that achieves both modality fusion and the capability to learn embeddings of unseen nodes.
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs
These techniques are different for homogeneous and heterogeneous networks because heterogeneous networks can have multiple types of edges and nodes as opposed to a homogeneous network.
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