mlsanityData to 7 Oct 2026

Interaction Order Prediction for Temporal Graphs

arXiv 2302.02128 ↗First posted 4 Feb 2023cs.SI cs.CL cs.LGcited by 0

Not on the map. Why?

Abstract (arXiv)

Link prediction in graphs is a task that has been widely investigated. It has been applied in various domains such as knowledge graph completion, content/item recommendation, social network recommendations and so on. The initial focus of most research was on link prediction in static graphs. However, there has recently been abundant work on modeling temporal graphs, and consequently one of the tasks that has been researched is link prediction in temporal graphs. However, most of the existing work does not focus on the order of link formation, and only predicts the existence of links. In this study, we aim to predict the order of node interactions.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 19 of its 34 references that are in the map. First posted on arXiv 4 Feb 2023.

Builds on

19 of 34 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Graph Attention Networkscited by 8,854
  4. 2017Inductive Representation Learning on Large Graphscited by 6,284
  5. 2019BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformercited by 952
  6. 2019CTRL: A Conditional Transformer Language Model for Controllable Generationcited by 754
  7. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  8. 2020Inductive Representation Learning on Temporal Graphscited by 454
  9. 2019Predicting Dynamic Embedding Trajectory in Temporal Interaction Networkscited by 443
  10. 2018A Comprehensive Survey of Deep Learning for Image Captioningcited by 213
  11. 2017Topology Adaptive Graph Convolutional Networkscited by 112
  12. 2020Knowledge Graph Embedding for Link Prediction: A Comparative Analysiscited by 85
7 more
  1. 2020A Survey of Deep Learning Techniques for Neural Machine Translationcited by 50
  2. 2018GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Predictioncited by 47
  3. 2018Neural Abstractive Text Summarization with Sequence-to-Sequence Modelscited by 39
  4. 2019E-LSTM-D: A Deep Learning Framework for Dynamic Network Link Predictioncited by 24
  5. 2022Instant Graph Neural Networks for Dynamic Graphscited by 16
  6. 2021Neural Clinical Event Sequence Prediction through Personalized Online Adaptive Learningcited by 3
  7. 2019Generative Temporal Link Prediction via Self-tokenized Sequence Modelingcited by 1

Built on by

Not cited yet.