Temporal graph learning for dynamic link prediction with text in online social networks

被引:6
作者
Dileo, Manuel [1 ]
Zignani, Matteo [1 ]
Gaito, Sabrina [1 ]
机构
[1] Univ Milan, Dept Comp Sci, Milan, Italy
关键词
Graph neural networks; Dynamic graphs; Network analysis; Online social networks;
D O I
10.1007/s10994-023-06475-x
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Link prediction in Online Social Networks-OSNs-has been the focus of numerous studies in the machine learning community. A successful machine learning-based solution for this task needs to (i) leverage global and local properties of the graph structure surrounding links; (ii) leverage the content produced by OSN users; and (iii) allow their representations to change over time, as thousands of new links between users and new content like textual posts, comments, images and videos are created/uploaded every month. Current works have successfully leveraged the structural information but only a few have also taken into account the textual content and/or the dynamicity of network structure and node attributes. In this paper, we propose a methodology based on temporal graph neural networks to handle the challenges described above. To understand the impact of textual content on this task, we provide a novel pipeline to include textual information alongside the structural one with the usage of BERT language models, dense preprocessing layers, and an effective post-processing decoder. We conducted the evaluation on a novel dataset gathered from an emerging blockchain-based online social network, using a live-update setting that takes into account the evolving nature of data and models. The dataset serves as a useful testing ground for link prediction evaluation because it provides high-resolution temporal information on link creation and textual content, characteristics hard to find in current benchmark datasets. Our results show that temporal graph learning is a promising solution for dynamic link prediction with text. Indeed, combining textual features and dynamic Graph Neural Networks-GNNs-leads to the best performances over time. On average, the textual content can enhance the performance of a dynamic GNN by 3.1% and, as the collection of documents increases in size over time, help even models that do not consider the structural information of the network.
引用
收藏
页码:2207 / 2226
页数:20
相关论文
共 39 条
  • [1] Fork-based user migration in Blockchain Online Social Media
    Ba, Cheick
    Michienzi, Andrea
    Guidi, Barbara
    Zignani, Matteo
    Ricci, Laura
    Gaito, Sabrina
    [J]. PROCEEDINGS OF THE 14TH ACM WEB SCIENCE CONFERENCE, WEBSCI 2022, 2022, : 174 - 184
  • [2] The role of cryptocurrency in the dynamics of blockchain-based social networks: The case of Steemit
    Ba, Cheick Tidiane
    Zignani, Matteo
    Gaito, Sabrina
    [J]. PLOS ONE, 2022, 17 (06):
  • [3] LP-ROBIN: Link prediction in dynamic networks exploiting incremental node embedding
    Barracchia, Emanuele Pio
    Pio, Gianvito
    Bifet, Albert
    Gomes, Heitor Murilo
    Pfahringer, Bernhard
    Ceci, Michelangelo
    [J]. INFORMATION SCIENCES, 2022, 606 : 702 - 721
  • [4] Bruss C. Bayan, 2019, 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), P126, DOI 10.1109/ICMLA.2019.00028
  • [5] Chung J., 2014, Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
  • [6] Devlin J, 2019, 2019 CONFERENCE OF THE NORTH AMERICAN CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS: HUMAN LANGUAGE TECHNOLOGIES (NAACL HLT 2019), VOL. 1, P4171
  • [7] Link Prediction with Text in Online Social Networks: The Role of Textual Content on High-Resolution Temporal Data
    Dileo, Manuel
    Ba, Cheick Tidiane
    Zignani, Matteo
    Gaito, Sabrina
    [J]. DISCOVERY SCIENCE (DS 2022), 2022, 13601 : 212 - 226
  • [8] Fey M., 2019, ARXIV
  • [9] Garimella K., 2021, P INT AAAI C WEB SOC, V15, P152, DOI 10.1609/icwsm.v15i1.18049
  • [10] An Overview of Blockchain Online Social Media from the Technical Point of View
    Guidi, Barbara
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (21):