For example, is the dynamic graph characterized by many large cliques which appear at fixed intervals of time, or perhaps by several large stars with dominant hubs that persist throughout? In this chapter we focus on these questions, and specifically on constructing concise summaries of large, ...
Awesome papers (codes) about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs) and their applications (i.e. Recommender Systems). Survey Papers 2025 Dynamic Graph Transformer with Correlated Spatial-Temporal Positional Encoding (WSDM, 2025) [paper][code] ...
Most analysis has been done on static graph embedding. Recently, however, some works have been devoted to studying dynamic graph embedding. Motivating example We consider a toy example to motivate the idea of capturing network dynamics. Consider an evolution of graph G, G={G1,..,GT}, where...
for example, pause treatment when the total tumor population is below a certain predefined threshold, and re-administer it again once the population of tumor cells recover to its initial size
Dynamic knowledge graph Derived information Data provenance Directed acyclic graph Smart cities 1. Introduction Inspired by Semantic Web technology, knowledge graphs are gaining popularity both in enterprise applications [1] and research fields [2]. They are seen as a suitable approach to integrating di...
For example, the assembly behavior in the workshop is often related to the use of tools. The bone nodes can only express the behavior of the human body, so the assembly action is often ignored. Graph convolutional networks are not only used to recognize human actions but also have good ...
Deep learning methods have also found applications in protein-related prediction. Their potent expressivity has proven increasingly pivotal in PPI site prediction in parallel with the exponential increase of biological data. For example, Zeng et al. [11] introduced DeepPPISP, a PPI site prediction ...
graph neural network only considers static spatial information. For example, GraphSAGE (Hamilton, Ying, and Leskovec 2017)21defines fixed neighbors and aggregates each node's neighborhood and own attributes. Wu et al.22proposed a graph-wave network that uses extended one-dimensional convolution to ...
To show how this graph could be created, the following example uses node A, a kernel upstream of the conditional node, B, to set the value of the conditional based on the results of work done by that kernel. The body of the conditional is populated using the graph API. ...
The graph structures consider incidence dynamic relationships of both inflows and outflows. Then we design a novel dynamic graph recurrent convolutional neural network model, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures ...