Real life situations such as travelling over time and across distance can be drawn on a graph. Exact distances and times are plotted on the graph. These are shown as co-ordinates. These points are ...
Researchers have combined the Dijkstra and Bellman-Ford algorithms to develop an even faster way to find the shortest paths ...
Abstract: Given a large graph, such as a social network or a knowledge graph, a fundamental query is how to find the distance from a source vertex to another vertex in the graph. As real graphs become ...
The climate crisis is amplifying displacement and making life harder for those already forced to flee. Climate change and displacement are increasingly interconnected. As extreme weather events and ...
Abstract: We present a linear-time subspace clustering approach that combines sparse representations and bipartite graph modeling. The signals are modeled as drawn from a union of low-dimensional ...
In this paper, we propose a Multilevel Graph Matching Network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. MGMN consists ...
Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting
Code for our SIGKDD'22 paper: "Pre-training-Enhanced Spatial-Temporal Graph Neural Network For Multivariate Time Series Forecasting". The code is developed with BasicTS, a PyTorch-based benchmark and ...
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