Instant Representation Learning for Recommendation over Large Dynamic Graphs

• URL: http://arxiv.org/abs/2305.18622v1
• Date: Mon, 22 May 2023 15:36:10 GMT
• Title: Instant Representation Learning for Recommendation over Large Dynamic Graphs
• Authors: Cheng Wu, Chaokun Wang, Jingcao Xu, Ziwei Fang, Tiankai Gu, Changping Wang, Yang Song, Kai Zheng, Xiaowei Wang, Guorui Zhou
• Abstract summary: We propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. For each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. To train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs.
• Score: 29.41179019520622
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recommender systems are able to learn user preferences based on user and item representations via their historical behaviors. To improve representation learning, recent recommendation models start leveraging information from various behavior types exhibited by users. In real-world scenarios, the user behavioral graph is not only multiplex but also dynamic, i.e., the graph evolves rapidly over time, with various types of nodes and edges added or deleted, which causes the Neighborhood Disturbance. Nevertheless, most existing methods neglect such streaming dynamics and thus need to be retrained once the graph has significantly evolved, making them unsuitable in the online learning environment. Furthermore, the Neighborhood Disturbance existing in dynamic graphs deteriorates the performance of neighbor-aggregation based graph models. To this end, we propose SUPA, a novel graph neural network for dynamic multiplex heterogeneous graphs. Compared to neighbor-aggregation architecture, SUPA develops a sample-update-propagate architecture to alleviate neighborhood disturbance. Specifically, for each new edge, SUPA samples an influenced subgraph, updates the representations of the two interactive nodes, and propagates the interaction information to the sampled subgraph. Furthermore, to train SUPA incrementally online, we propose InsLearn, an efficient workflow for single-pass training of large dynamic graphs. Extensive experimental results on six real-world datasets show that SUPA has a good generalization ability and is superior to sixteen state-of-the-art baseline methods. The source code is available at https://github.com/shatter15/SUPA.

Related papers

• A Graph-Enhanced Click Model for Web Search [67.27218481132185]
  We propose a novel graph-enhanced click model (GraphCM) for web search. We exploit both intra-session and inter-session information for the sparsity and cold-start problems.
  arXiv  Detail & Related papers  (2022-06-17T08:32:43Z)
• Instant Graph Neural Networks for Dynamic Graphs [18.916632816065935]
  We propose Instant Graph Neural Network (InstantGNN), an incremental approach for the graph representation matrix of dynamic graphs. Our method avoids time-consuming, repetitive computations and allows instant updates on the representation and instant predictions. Our model achieves state-of-the-art accuracy while having orders-of-magnitude higher efficiency than existing methods.
  arXiv  Detail & Related papers  (2022-06-03T03:27:42Z)
• Neural Graph Matching for Pre-training Graph Neural Networks [72.32801428070749]
  Graph neural networks (GNNs) have been shown powerful capacity at modeling structural data. We present a novel Graph Matching based GNN Pre-Training framework, called GMPT. The proposed method can be applied to fully self-supervised pre-training and coarse-grained supervised pre-training.
  arXiv  Detail & Related papers  (2022-03-03T09:53:53Z)
• Deep Dynamic Effective Connectivity Estimation from Multivariate Time Series [0.0]
  We develop dynamic effective connectivity estimation via neural network training (DECENNT) DECENNT outperforms state-of-the-art (SOTA) methods on five different tasks and infers interpretable task-specific dynamic graphs.
  arXiv  Detail & Related papers  (2022-02-04T21:14:21Z)
• Dynamic Graph Learning-Neural Network for Multivariate Time Series Modeling [2.3022070933226217]
  We propose a novel framework, namely static- and dynamic-graph learning-neural network (GL) The model acquires static and dynamic graph matrices from data to model long-term and short-term patterns respectively. It achieves state-of-the-art performance on almost all datasets.
  arXiv  Detail & Related papers  (2021-12-06T08:19:15Z)
• Dynamic Graph Representation Learning via Graph Transformer Networks [41.570839291138114]
  We propose a Transformer-based dynamic graph learning method named Dynamic Graph Transformer (DGT) DGT has spatial-temporal encoding to effectively learn graph topology and capture implicit links. We show that DGT presents superior performance compared with several state-of-the-art baselines.
  arXiv  Detail & Related papers  (2021-11-19T21:44:23Z)
• 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)
• TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
  We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion. To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
  arXiv  Detail & Related papers  (2021-05-17T15:33:25Z)
• Graph Contrastive Learning with Adaptive Augmentation [23.37786673825192]
  We propose a novel graph contrastive representation learning method with adaptive augmentation. Specifically, we design augmentation schemes based on node centrality measures to highlight important connective structures. Our proposed method consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts.
  arXiv  Detail & Related papers  (2020-10-27T15:12:21Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)
• Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach [55.44107800525776]
  Graph Convolutional Networks (GCNs) are state-of-the-art graph based representation learning models. In this paper, we revisit GCN based Collaborative Filtering (CF) based Recommender Systems (RS) We show that removing non-linearities would enhance recommendation performance, consistent with the theories in simple graph convolutional networks. We propose a residual network structure that is specifically designed for CF with user-item interaction modeling.
  arXiv  Detail & Related papers  (2020-01-28T04:41:25Z)

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.