TimeGraphs: Graph-based Temporal Reasoning

• URL: http://arxiv.org/abs/2401.03134v1
• Date: Sat, 6 Jan 2024 06:26:49 GMT
• Title: TimeGraphs: Graph-based Temporal Reasoning
• Authors: Paridhi Maheshwari, Hongyu Ren, Yanan Wang, Rok Sosic, Jure Leskovec
• Abstract summary: TimeGraphs is a novel approach that characterizes dynamic interactions as a hierarchical temporal graph. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset.
• Score: 64.18083371645956
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world systems exhibit temporal, dynamic behaviors, which are captured as time series of complex agent interactions. To perform temporal reasoning, current methods primarily encode temporal dynamics through simple sequence-based models. However, in general these models fail to efficiently capture the full spectrum of rich dynamics in the input, since the dynamics is not uniformly distributed. In particular, relevant information might be harder to extract and computing power is wasted for processing all individual timesteps, even if they contain no significant changes or no new information. Here we propose TimeGraphs, a novel approach that characterizes dynamic interactions as a hierarchical temporal graph, diverging from traditional sequential representations. Our approach models the interactions using a compact graph-based representation, enabling adaptive reasoning across diverse time scales. Adopting a self-supervised method, TimeGraphs constructs a multi-level event hierarchy from a temporal input, which is then used to efficiently reason about the unevenly distributed dynamics. This construction process is scalable and incremental to accommodate streaming data. We evaluate TimeGraphs on multiple datasets with complex, dynamic agent interactions, including a football simulator, the Resistance game, and the MOMA human activity dataset. The results demonstrate both robustness and efficiency of TimeGraphs on a range of temporal reasoning tasks. Our approach obtains state-of-the-art performance and leads to a performance increase of up to 12.2% on event prediction and recognition tasks over current approaches. Our experiments further demonstrate a wide array of capabilities including zero-shot generalization, robustness in case of data sparsity, and adaptability to streaming data flow.

Related papers

• Decoupled Graph Neural Networks for Large Dynamic Graphs [14.635923016087503]
  We propose a decoupled graph neural network for large dynamic graphs. We show that our algorithm achieves state-of-the-art performance in both kinds of dynamic graphs.
  arXiv  Detail & Related papers  (2023-05-14T23:00:10Z)
• Temporal Aggregation and Propagation Graph Neural Networks for Dynamic Representation [67.26422477327179]
  Temporal graphs exhibit dynamic interactions between nodes over continuous time. We propose a novel method of temporal graph convolution with the whole neighborhood. Our proposed TAP-GNN outperforms existing temporal graph methods by a large margin in terms of both predictive performance and online inference latency.
  arXiv  Detail & Related papers  (2023-04-15T08:17:18Z)
• DyTed: Disentangled Representation Learning for Discrete-time Dynamic Graph [59.583555454424]
  We propose a novel disenTangled representation learning framework for discrete-time Dynamic graphs, namely DyTed. We specially design a temporal-clips contrastive learning task together with a structure contrastive learning to effectively identify the time-invariant and time-varying representations respectively.
  arXiv  Detail & Related papers  (2022-10-19T14:34:12Z)
• Time-aware Dynamic Graph Embedding for Asynchronous Structural Evolution [60.695162101159134]
  Existing works merely view a dynamic graph as a sequence of changes. We formulate dynamic graphs as temporal edge sequences associated with joining time of. vertex and timespan of edges. A time-aware Transformer is proposed to embed. vertex' dynamic connections and ToEs into the learned. vertex representations.
  arXiv  Detail & Related papers  (2022-07-01T15:32:56Z)
• Learning the Evolutionary and Multi-scale Graph Structure for Multivariate Time Series Forecasting [50.901984244738806]
  We show how to model the evolutionary and multi-scale interactions of time series. In particular, we first provide a hierarchical graph structure cooperated with the dilated convolution to capture the scale-specific correlations. A unified neural network is provided to integrate the components above to get the final prediction.
  arXiv  Detail & Related papers  (2022-06-28T08:11:12Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series [0.0]
  We develop dynamic effective connectivity estimation via neural network training (DECENNT) DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs.
  arXiv  Detail & Related papers  (2022-02-04T21:14:21Z)
• Efficient-Dyn: Dynamic Graph Representation Learning via Event-based Temporal Sparse Attention Network [2.0047096160313456]
  Dynamic graph neural networks have received more and more attention from researchers. We propose a novel dynamic graph neural network, Efficient-Dyn. It adaptively encodes temporal information into a sequence of patches with an equal amount of temporal-topological structure.
  arXiv  Detail & Related papers  (2022-01-04T23:52:24Z)
• Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling [2.3022070933226217]
  We propose a novel framework, namely static- and dynamic-graph learning-neural network (GL) The model acquires static and dynamic graph matrices from data to model long-term and short-term patterns respectively. It achieves state-of-the-art performance on almost all datasets.
  arXiv  Detail & Related papers  (2021-12-06T08:19:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.