Time-aware Graph Embedding: A temporal smoothness and task-oriented approach

• URL: http://arxiv.org/abs/2007.11164v1
• Date: Wed, 22 Jul 2020 02:20:25 GMT
• Title: Time-aware Graph Embedding: A temporal smoothness and task-oriented approach
• Authors: Yonghui Xu, Shengjie Sun, Yuan Miao, Dong Yang, Xiaonan Meng, Yi Hu, Ke Wang, Hengjie Song, Chuanyan Miao
• Abstract summary: This paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information.
• Score: 9.669206664225234
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge graph embedding, which aims to learn the low-dimensional representations of entities and relationships, has attracted considerable research efforts recently. However, most knowledge graph embedding methods focus on the structural relationships in fixed triples while ignoring the temporal information. Currently, existing time-aware graph embedding methods only focus on the factual plausibility, while ignoring the temporal smoothness which models the interactions between a fact and its contexts, and thus can capture fine-granularity temporal relationships. This leads to the limited performance of embedding related applications. To solve this problem, this paper presents a Robustly Time-aware Graph Embedding (RTGE) method by incorporating temporal smoothness. Two major innovations of our paper are presented here. At first, RTGE integrates a measure of temporal smoothness in the learning process of the time-aware graph embedding. Via the proposed additional smoothing factor, RTGE can preserve both structural information and evolutionary patterns of a given graph. Secondly, RTGE provides a general task-oriented negative sampling strategy associated with temporally-aware information, which further improves the adaptive ability of the proposed algorithm and plays an essential role in obtaining superior performance in various tasks. Extensive experiments conducted on multiple benchmark tasks show that RTGE can increase performance in entity/relationship/temporal scoping prediction tasks.

Related papers

• Instance-Aware Graph Prompt Learning [71.26108600288308]
  We introduce Instance-Aware Graph Prompt Learning (IA-GPL) in this paper. The process involves generating intermediate prompts for each instance using a lightweight architecture. Experiments conducted on multiple datasets and settings showcase the superior performance of IA-GPL compared to state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-11-26T18:38:38Z)
• TimeGraphs: Graph-based Temporal Reasoning [64.18083371645956]
  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.
  arXiv  Detail & Related papers  (2024-01-06T06:26:49Z)
• MTGER: Multi-view Temporal Graph Enhanced Temporal Reasoning over Time-Involved Document [26.26604509399347]
  MTGER is a novel framework for temporal reasoning over time-involved documents. It explicitly models the temporal relationships among facts by multi-view temporal graphs. We show that MTGER gives more consistent answers under question perturbations.
  arXiv  Detail & Related papers  (2023-11-08T16:41:37Z)
• Time-aware Graph Structure Learning via Sequence Prediction on Temporal Graphs [10.034072706245544]
  We propose a Time-aware Graph Structure Learning (TGSL) approach via sequence prediction on temporal graphs. In particular, it predicts time-aware context embedding and uses the Gumble-Top-K to select the closest candidate edges to this context embedding. Experiments on temporal link prediction benchmarks demonstrate that TGSL yields significant gains for the popular TGNs such as TGAT and GraphMixer.
  arXiv  Detail & Related papers  (2023-06-13T11:34:36Z)
• FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation Learning [47.52733127616005]
  We propose a Frame-level Timeline Modeling (FTM) method that helps to capture both short-term and long-term features. Our method can be easily assembled with most temporal GNNs.
  arXiv  Detail & Related papers  (2023-02-23T06:53:16Z)
• 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)
• Temporal Knowledge Graph Reasoning with Low-rank and Model-agnostic Representations [1.8262547855491458]
  We introduce Time-LowFER, a family of parameter-efficient and time-aware extensions of the low-rank tensor factorization model LowFER. Noting several limitations in current approaches to represent time, we propose a cycle-aware time-encoding scheme for time features. We implement our methods in a unified temporal knowledge graph embedding framework, focusing on time-sensitive data processing.
  arXiv  Detail & Related papers  (2022-04-10T22:24:11Z)
• ChronoR: Rotation Based Temporal Knowledge Graph Embedding [8.039202293739185]
  We study the challenging problem of inference over temporal knowledge graphs. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.
  arXiv  Detail & Related papers  (2021-03-18T17:08:33Z)
• Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval [83.56444443849679]
  This work takes advantage of the temporal dependencies within videos and proposes a novel self-supervised method named Temporal Contrastive Graph Learning (TCGL) Our TCGL roots in a hybrid graph contrastive learning strategy to jointly regard the inter-snippet and intra-snippet temporal dependencies as self-supervision signals for temporal representation learning. Experimental results demonstrate the superiority of our TCGL over the state-of-the-art methods on large-scale action recognition and video retrieval benchmarks.
  arXiv  Detail & Related papers  (2021-01-04T08:11:39Z)
• One-shot Learning for Temporal Knowledge Graphs [49.41854171118697]
  We propose a one-shot learning framework for link prediction in temporal knowledge graphs. Our proposed method employs a self-attention mechanism to effectively encode temporal interactions between entities. Our experiments show that the proposed algorithm outperforms the state of the art baselines for two well-studied benchmarks.
  arXiv  Detail & Related papers  (2020-10-23T03:24:44Z)

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.