Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation

• URL: http://arxiv.org/abs/2205.14651v1
• Date: Sun, 29 May 2022 13:14:53 GMT
• Title: Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation
• Authors: Guillaume Salha-Galvan
• Abstract summary: Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful groups of unsupervised node embedding methods. At the beginning of this Ph.D. project, GAE and VGAE models were also suffering from key limitations, preventing them from being adopted in the industry. We present several contributions to improve these models, with the general aim of facilitating their use to address industrial-level problems involving graph representations.
• Score: 1.2691047660244335
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as two powerful groups of unsupervised node embedding methods, with various applications to graph-based machine learning problems such as link prediction and community detection. Nonetheless, at the beginning of this Ph.D. project, GAE and VGAE models were also suffering from key limitations, preventing them from being adopted in the industry. In this thesis, we present several contributions to improve these models, with the general aim of facilitating their use to address industrial-level problems involving graph representations. Firstly, we propose two strategies to overcome the scalability issues of previous GAE and VGAE models, permitting to effectively train these models on large graphs with millions of nodes and edges. These strategies leverage graph degeneracy and stochastic subgraph decoding techniques, respectively. Besides, we introduce Gravity-Inspired GAE and VGAE, providing the first extensions of these models for directed graphs, that are ubiquitous in industrial applications. We also consider extensions of GAE and VGAE models for dynamic graphs. Furthermore, we argue that GAE and VGAE models are often unnecessarily complex, and we propose to simplify them by leveraging linear encoders. Lastly, we introduce Modularity-Aware GAE and VGAE to improve community detection on graphs, while jointly preserving good performances on link prediction. In the last part of this thesis, we evaluate our methods on several graphs extracted from the music streaming service Deezer. We put the emphasis on graph-based music recommendation problems. In particular, we show that our methods can improve the detection of communities of similar musical items to recommend to users, that they can effectively rank similar artists in a cold start setting, and that they permit modeling the music genre perception across cultures.

Related papers

• Can Modifying Data Address Graph Domain Adaptation? [20.343259091425708]
  Unsupervised Graph Domain Adaptation (UGDA) aims to facilitate knowledge transfer from a labeled source graph to an unlabeled target graph. We propose GraphAlign, a novel UGDA method that generates a small yet transferable graph. By exclusively training a GNN on this new graph with classic Empirical Risk Minimization (ERM), GraphAlign attains exceptional performance on the target graph.
  arXiv  Detail & Related papers  (2024-07-27T17:56:31Z)
• Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification [10.497590357666114]
  We propose the Self-Label Augmented VGAE model for inductive graph representation learning. To leverage the label information for training, our model takes node labels as one-hot encoded inputs and then performs label reconstruction in model training. Our proposed model archives promise results on node classification with particular superiority under semi-supervised learning settings.
  arXiv  Detail & Related papers  (2024-03-26T08:59:37Z)
• Graph Domain Adaptation: Challenges, Progress and Prospects [61.9048172631524]
  We propose graph domain adaptation as an effective knowledge-transfer paradigm across graphs. GDA introduces a bunch of task-related graphs as source graphs and adapts the knowledge learnt from source graphs to the target graphs. We outline the research status and challenges, propose a taxonomy, introduce the details of representative works, and discuss the prospects.
  arXiv  Detail & Related papers  (2024-02-01T02:44:32Z)
• New Frontiers in Graph Autoencoders: Joint Community Detection and Link Prediction [27.570978996576503]
  Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction (LP) It is still unclear to what extent one can improve CD with GAE and VGAE, especially in the absence of node features. We show that jointly addressing these two tasks with high accuracy is possible.
  arXiv  Detail & Related papers  (2022-11-16T15:26:56Z)
• Graph Generative Model for Benchmarking Graph Neural Networks [73.11514658000547]
  We introduce a novel graph generative model that learns and reproduces the distribution of real-world graphs in a privacy-controlled way. Our model can successfully generate privacy-controlled, synthetic substitutes of large-scale real-world graphs that can be effectively used to benchmark GNN models.
  arXiv  Detail & Related papers  (2022-07-10T06:42:02Z)
• Modularity-Aware Graph Autoencoders for Joint Community Detection and Link Prediction [27.570978996576503]
  Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as powerful methods for link prediction. It is still unclear to which extent one can improve community detection with GAE and VGAE. We show that jointly addressing these two tasks with high accuracy is possible.
  arXiv  Detail & Related papers  (2022-02-02T11:07:11Z)
• Towards Graph Self-Supervised Learning with Contrastive Adjusted Zooming [48.99614465020678]
  We introduce a novel self-supervised graph representation learning algorithm via Graph Contrastive Adjusted Zooming. This mechanism enables G-Zoom to explore and extract self-supervision signals from a graph from multiple scales. We have conducted extensive experiments on real-world datasets, and the results demonstrate that our proposed model outperforms state-of-the-art methods consistently.
  arXiv  Detail & Related papers  (2021-11-20T22:45:53Z)
• On Generalization of Graph Autoencoders with Adversarial Training [8.608288231153304]
  Adversarial training is an approach for increasing model's resilience against adversarial perturbations. We formulate L2 and L1 versions of adversarial training in two powerful node embedding methods. We demonstrate that both L2 and L1 adversarial training boost the generalization of GAE and VGAE.
  arXiv  Detail & Related papers  (2021-07-06T14:53:19Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Diversified Multiscale Graph Learning with Graph Self-Correction [55.43696999424127]
  We propose a diversified multiscale graph learning model equipped with two core ingredients. A graph self-correction (GSC) mechanism to generate informative embedded graphs, and a diversity boosting regularizer (DBR) to achieve a comprehensive characterization of the input graph. Experiments on popular graph classification benchmarks show that the proposed GSC mechanism leads to significant improvements over state-of-the-art graph pooling methods.
  arXiv  Detail & Related papers  (2021-03-17T16:22:24Z)
• Dirichlet Graph Variational Autoencoder [65.94744123832338]
  We present Dirichlet Graph Variational Autoencoder (DGVAE) with graph cluster memberships as latent factors. Motivated by the low pass characteristics in balanced graph cut, we propose a new variant of GNN named Heatts to encode the input graph into cluster memberships.
  arXiv  Detail & Related papers  (2020-10-09T07:35:26Z)

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.