Zobrazeno 1 - 10
of 11 103
pro vyhledávání: '"DYNAMIC GRAPHS"'
Generative self-supervised learning (SSL), especially masked autoencoders (MAE), has greatly succeeded and garnered substantial research interest in graph machine learning. However, the research of MAE in dynamic graphs is still scant. This gap is pr
Externí odkaz:
http://arxiv.org/abs/2409.09262
Fully connected Graph Transformers (GT) have rapidly become prominent in the static graph community as an alternative to Message-Passing models, which suffer from a lack of expressivity, oversquashing, and under-reaching. However, in a dynamic contex
Externí odkaz:
http://arxiv.org/abs/2409.17986
Detecting anomalies in a temporal sequence of graphs can be applied is areas such as the detection of accidents in transport networks and cyber attacks in computer networks. Existing methods for detecting abnormal graphs can suffer from multiple limi
Externí odkaz:
http://arxiv.org/abs/2410.05687
This study introduces a global stock market volatility forecasting model that enhances forecasting accuracy and practical utility in real-world financial decision-making by integrating dynamic graph structures and encompassing the union of active tra
Externí odkaz:
http://arxiv.org/abs/2409.15320
Autor:
Kim, Joohee, Lee, Hyunwook, Nguyen, Duc M., Shin, Minjeong, Kwon, Bum Chul, Ko, Sungahn, Elmqvist, Niklas
Comics are an effective method for sequential data-driven storytelling, especially for dynamic graphs -- graphs whose vertices and edges change over time. However, manually creating such comics is currently time-consuming, complex, and error-prone. I
Externí odkaz:
http://arxiv.org/abs/2408.04874
Autor:
Sahu, Subhajit
PageRank is a metric that assigns importance to the vertices of a graph based on its neighbors and their scores. Recently, there has been increasing interest in computing PageRank on dynamic graphs, where the graph structure evolves due to edge inser
Externí odkaz:
http://arxiv.org/abs/2407.19562
Autor:
Ding, Zifeng, Li, Yifeng, He, Yuan, Norelli, Antonio, Wu, Jingcheng, Tresp, Volker, Ma, Yunpu, Bronstein, Michael
Learning useful representations for continuous-time dynamic graphs (CTDGs) is challenging, due to the concurrent need to span long node interaction histories and grasp nuanced temporal details. In particular, two problems emerge: (1) Encoding longer
Externí odkaz:
http://arxiv.org/abs/2408.04713
Autor:
Eddin, Ahmad Naser, Bono, Jacopo, Aparício, David, Ferreira, Hugo, Ribeiro, Pedro, Bizarro, Pedro
Continuous-time dynamic graphs (CTDGs) are essential for modeling interconnected, evolving systems. Traditional methods for extracting knowledge from these graphs often depend on feature engineering or deep learning. Feature engineering is limited by
Externí odkaz:
http://arxiv.org/abs/2407.07712
Learning Continuous-Time Dynamic Graphs (C-TDGs) requires accurately modeling spatio-temporal information on streams of irregularly sampled events. While many methods have been proposed recently, we find that most message passing-, recurrent- or self
Externí odkaz:
http://arxiv.org/abs/2406.02740
Autor:
Li, Dongyuan, Tan, Shiyin, Zhang, Ying, Jin, Ming, Pan, Shirui, Okumura, Manabu, Jiang, Renhe
Dynamic graph learning aims to uncover evolutionary laws in real-world systems, enabling accurate social recommendation (link prediction) or early detection of cancer cells (classification). Inspired by the success of state space models, e.g., Mamba,
Externí odkaz:
http://arxiv.org/abs/2408.06966