FDGNN: Fully Dynamic Graph Neural Network
- URL: http://arxiv.org/abs/2206.03469v1
- Date: Tue, 7 Jun 2022 17:40:51 GMT
- Title: FDGNN: Fully Dynamic Graph Neural Network
- Authors: Alice Moallemy-Oureh, Silvia Beddar-Wiesing, R\"udiger Nather,
Josephine M. Thomas
- Abstract summary: We present a novel Fully Dynamic Graph Neural Network (FDGNN) that can handle fully-dynamic graphs in continuous time.
The proposed method provides a node and an edge embedding that includes their activity to address added and deleted nodes or edges, and possible attributes.
Our model can be updated efficiently by considering single events for local retraining.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Dynamic Graph Neural Networks recently became more and more important as
graphs from many scientific fields, ranging from mathematics, biology, social
sciences, and physics to computer science, are dynamic by nature. While
temporal changes (dynamics) play an essential role in many real-world
applications, most of the models in the literature on Graph Neural Networks
(GNN) process static graphs. The few GNN models on dynamic graphs only consider
exceptional cases of dynamics, e.g., node attribute-dynamic graphs or
structure-dynamic graphs limited to additions or changes to the graph's edges,
etc. Therefore, we present a novel Fully Dynamic Graph Neural Network (FDGNN)
that can handle fully-dynamic graphs in continuous time. The proposed method
provides a node and an edge embedding that includes their activity to address
added and deleted nodes or edges, and possible attributes. Furthermore, the
embeddings specify Temporal Point Processes for each event to encode the
distributions of the structure- and attribute-related incoming graph events. In
addition, our model can be updated efficiently by considering single events for
local retraining.
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