A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities

• URL: http://arxiv.org/abs/2505.04461v1
• Date: Wed, 07 May 2025 14:31:10 GMT
• Title: A Survey on Temporal Interaction Graph Representation Learning: Progress, Challenges, and Opportunities
• Authors: Pengfei Jiao, Hongjiang Chen, Xuan Guo, Zhidong Zhao, Dongxiao He, Di Jin,
• Abstract summary: temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years.<n>TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information.<n>We propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process.
• Score: 16.99145758730107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Temporal interaction graphs (TIGs), defined by sequences of timestamped interaction events, have become ubiquitous in real-world applications due to their capability to model complex dynamic system behaviors. As a result, temporal interaction graph representation learning (TIGRL) has garnered significant attention in recent years. TIGRL aims to embed nodes in TIGs into low-dimensional representations that effectively preserve both structural and temporal information, thereby enhancing the performance of downstream tasks such as classification, prediction, and clustering within constantly evolving data environments. In this paper, we begin by introducing the foundational concepts of TIGs and emphasize the critical role of temporal dependencies. We then propose a comprehensive taxonomy of state-of-the-art TIGRL methods, systematically categorizing them based on the types of information utilized during the learning process to address the unique challenges inherent to TIGs. To facilitate further research and practical applications, we curate the source of datasets and benchmarks, providing valuable resources for empirical investigations. Finally, we examine key open challenges and explore promising research directions in TIGRL, laying the groundwork for future advancements that have the potential to shape the evolution of this field.

Related papers

• T-GRAB: A Synthetic Diagnostic Benchmark for Learning on Temporal Graphs [6.199165061105655]
  We introduce the Temporal Graph Reasoning Benchmark (T-GRAB) to systematically probe the capabilities of TGNNs to reason across time.<n>T-GRAB provides controlled, interpretable tasks that isolate key temporal skills.<n>We evaluate 11 temporal graph learning methods on these tasks, revealing fundamental shortcomings in their ability to generalize temporal patterns.
  arXiv  Detail & Related papers  (2025-07-14T11:47:43Z)
• STRAP: Spatio-Temporal Pattern Retrieval for Out-of-Distribution Generalization [34.53308463024231]
  We propose an innovative Spatio-Temporal Retrieval-Augmented Pattern Learning framework, STRAP.<n>During inference, STRAP retrieves relevant patterns from this library based on similarity to the current input and injects them into the model via a plug-and-play prompting mechanism.<n>Experiments across multiple real-world streaming graph datasets show that STRAP consistently outperforms state-of-the-art STGNN baselines on STOOD tasks.
  arXiv  Detail & Related papers  (2025-05-26T06:11:05Z)
• Mind the truncation gap: challenges of learning on dynamic graphs with recurrent architectures [10.434476078553786]
  Continuous-time dynamic graphs (CTDGs) pose challenges for machine learning (ML) approaches.<n>We show that a short truncation of backpropagation-through-time (BPTT) can limit the learning of dependencies beyond a single hop.<n>We argue that understanding and addressing this gap is essential as the importance of CTDGs grows.
  arXiv  Detail & Related papers  (2024-12-30T16:07:41Z)
• Large Models for Time Series and Spatio-Temporal Data: A Survey and Outlook [95.32949323258251]
  Temporal data, notably time series andtemporal-temporal data, are prevalent in real-world applications. Recent advances in large language and other foundational models have spurred increased use in time series andtemporal data mining.
  arXiv  Detail & Related papers  (2023-10-16T09:06:00Z)
• A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects [73.44022660932087]
  temporal characteristics are prominently evident in a substantial volume of knowledge. The continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset are cited. The task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information.
  arXiv  Detail & Related papers  (2023-08-04T16:49:54Z)
• Deep learning for dynamic graphs: models and benchmarks [16.851689741256912]
  Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved.
  arXiv  Detail & Related papers  (2023-07-12T12:02:36Z)
• ALP: Action-Aware Embodied Learning for Perception [60.64801970249279]
  We introduce Action-Aware Embodied Learning for Perception (ALP) ALP incorporates action information into representation learning through a combination of optimizing a reinforcement learning policy and an inverse dynamics prediction objective. We show that ALP outperforms existing baselines in several downstream perception tasks.
  arXiv  Detail & Related papers  (2023-06-16T21:51:04Z)
• Deep Temporal Graph Clustering [77.02070768950145]
  We propose a general framework for deep Temporal Graph Clustering (GC) GC introduces deep clustering techniques to suit the interaction sequence-based batch-processing pattern of temporal graphs. Our framework can effectively improve the performance of existing temporal graph learning methods.
  arXiv  Detail & Related papers  (2023-05-18T06:17:50Z)
• A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal [57.8455911689554]
  Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs) It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
  arXiv  Detail & Related papers  (2022-12-12T08:40:04Z)
• Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition [88.34182299496074]
  Action labels are only available on a source dataset, but unavailable on a target dataset in the training stage. We utilize a self-supervision scheme to reduce the domain shift between two skeleton-based action datasets. By segmenting and permuting temporal segments or human body parts, we design two self-supervised learning classification tasks.
  arXiv  Detail & Related papers  (2022-07-17T07:05:39Z)
• An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning [14.353798949041698]
  We propose an adaptive federated relevance framework, namely FedRel, for spatial-temporal graph learning. The core Dynamic Inter-Intra Graph (DIIG) module in the framework is able to use these features to generate the spatial-temporal graphs. To improve the model generalization ability and performance while preserving the local data privacy, we also design a relevance-driven federated learning module.
  arXiv  Detail & Related papers  (2022-06-07T16:12:17Z)
• A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [79.48829365560788]
  Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios. HG embedding aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks.
  arXiv  Detail & Related papers  (2020-11-30T15:03:47Z)

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.