葡萄糖
发表于 2025-3-28 16:34:06
On Compressing Historical Cliques in Temporal Graphsd bioinformatics. Many real-world graphs change over time, with edges arriving continuously, and each edge has a timestamp representing the arrival time of that edge; such graphs are also known as temporal graphs. All maximal cliques in all snapshots since all possible historical moments are called
没血色
发表于 2025-3-28 22:36:12
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体贴
发表于 2025-3-29 00:58:55
MIPM: A Multidimensional Information Perception Model for Estimating Time of Arrival on Real Road Nees. Thus, despite many existing works focusing on improving the efficiency and accuracy of the transportation system, however, few of them can handle multidimensional features on road networks. In this paper, we focus on a famous problem of the intelligent transportation system named estimated time
代理人
发表于 2025-3-29 06:21:23
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demote
发表于 2025-3-29 10:03:10
TimeGAE: A Multivariate Time-Series Generation Method via Graph Auto Encoders this issue, time series generation methods have emerged as a promising approach to alleviate data scarcity. However, most existing methods do not explicitly consider multivariate time series, thereby failing to fully exploit the potential spatial dependencies among different variables. The ability
过分自信
发表于 2025-3-29 13:54:52
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积极词汇
发表于 2025-3-29 18:33:03
Simulating Individual Infection Risk over Big Trajectory Data attention. They are helpful in predicting epidemic transmission trends and mitigating the spread of infectious diseases. In this light, we study a new problem of Individual Infection Risk Assessment (IIRA) on the basis of fine-grained trajectory data. The problem aims to quantify the infection risk
aptitude
发表于 2025-3-29 19:56:09
Flexible Contact Correlation Learning on Spatio-Temporal Trajectoriesch or tracing, aiming to identify all trajectories in contact with a query trajectory. However, these studies only consider spatial contacts at specific timestamps, and highly rely on precise data with consistent sampling rates and aligned timestamps. In light of these limitations, we investigate th
depreciate
发表于 2025-3-30 02:47:58
Inductive Spatial Temporal Prediction Under Data Drift with Informative Graph Neural Networkms, stock markets). However, external events (e.g., urban structural growth, market crash) and emerging new entities (e.g., locations, stocks) can undermine prediction accuracy by inducing data drift over time. Most existing studies extract invariant patterns to counter data drift but ignore pattern
内行
发表于 2025-3-30 06:43:56
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