InstructG2I: Synthesizing Images from Multimodal Attributed Graphs

• URL: http://arxiv.org/abs/2410.07157v1
• Date: Wed, 9 Oct 2024 17:56:15 GMT
• Title: InstructG2I: Synthesizing Images from Multimodal Attributed Graphs
• Authors: Bowen Jin, Ziqi Pang, Bingjun Guo, Yu-Xiong Wang, Jiaxuan You, Jiawei Han,
• Abstract summary: We propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling. A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
• Score: 50.852150521561676
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs). This task poses significant challenges due to the explosion in graph size, dependencies among graph entities, and the need for controllability in graph conditions. To address these challenges, we propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling by combining personalized page rank and re-ranking based on vision-language features. Then, a Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process of diffusion. Finally, we propose graph classifier-free guidance, enabling controllable generation by varying the strength of graph guidance and multiple connected edges to a node. Extensive experiments conducted on three datasets from different domains demonstrate the effectiveness and controllability of our approach. The code is available at https://github.com/PeterGriffinJin/InstructG2I.

Related papers

• Graph Mixup with Soft Alignments [49.61520432554505]
  We study graph data augmentation by mixup, which has been used successfully on images. We propose S-Mixup, a simple yet effective mixup method for graph classification by soft alignments.
  arXiv  Detail & Related papers  (2023-06-11T22:04:28Z)
• SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs [4.550112751061436]
  We describe SynGraphy, a method for visually summarising the structure of large network datasets. It works by drawing smaller graphs generated to have similar structural properties to the input graphs.
  arXiv  Detail & Related papers  (2023-02-15T16:00:15Z)
• 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)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• 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)
• Accurate Learning of Graph Representations with Graph Multiset Pooling [45.72542969364438]
  We propose a Graph Multiset Transformer (GMT) that captures the interaction between nodes according to their structural dependencies. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks.
  arXiv  Detail & Related papers  (2021-02-23T07:45:58Z)
• MathNet: Haar-Like Wavelet Multiresolution-Analysis for Graph Representation and Learning [31.42901131602713]
  We propose a framework for graph neural networks with multiresolution Haar-like wavelets, or MathNet, with interrelated convolution and pooling strategies. The proposed MathNet outperforms various existing GNN models, especially on big data sets.
  arXiv  Detail & Related papers  (2020-07-22T05:00:59Z)
• Second-Order Pooling for Graph Neural Networks [62.13156203025818]
  We propose to use second-order pooling as graph pooling, which naturally solves the above challenges. We show that direct use of second-order pooling with graph neural networks leads to practical problems. We propose two novel global graph pooling methods based on second-order pooling; namely, bilinear mapping and attentional second-order pooling.
  arXiv  Detail & Related papers  (2020-07-20T20:52:36Z)
• Graph Representation Learning Network via Adaptive Sampling [4.996520403438455]
  Graph Attention Network (GAT) and GraphSAGE are neural network architectures that operate on graph-structured data. One challenge raised by GraphSAGE is how to smartly combine neighbour features based on graph structure. We propose a new architecture to address these issues that is more efficient and is capable of incorporating different edge type information.
  arXiv  Detail & Related papers  (2020-06-08T14:36:20Z)

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.