Graph Summarization with Graph Neural Networks

• URL: http://arxiv.org/abs/2203.05919v1
• Date: Fri, 11 Mar 2022 13:45:34 GMT
• Title: Graph Summarization with Graph Neural Networks
• Authors: Maximilian Blasi and Manuel Freudenreich and Johannes Horvath and David Richerby and Ansgar Scherp
• Abstract summary: We use Graph Neural Networks to represent large graphs in a structured and compact way. We compare different GNNs with a standard multi-layer perceptron (MLP) and Bloom filter as non-neural method. Our results show that the performance of GNNs are close to each other.
• Score: 2.449909275410288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The goal of graph summarization is to represent large graphs in a structured and compact way. A graph summary based on equivalence classes preserves pre-defined features of a graph's vertex within a $k$-hop neighborhood such as the vertex labels and edge labels. Based on these neighborhood characteristics, the vertex is assigned to an equivalence class. The calculation of the assigned equivalence class must be a permutation invariant operation on the pre-defined features. This is achieved by sorting on the feature values, e. g., the edge labels, which is computationally expensive, and subsequently hashing the result. Graph Neural Networks (GNN) fulfill the permutation invariance requirement. We formulate the problem of graph summarization as a subgraph classification task on the root vertex of the $k$-hop neighborhood. We adapt different GNN architectures, both based on the popular message-passing protocol and alternative approaches, to perform the structural graph summarization task. We compare different GNNs with a standard multi-layer perceptron (MLP) and Bloom filter as non-neural method. For our experiments, we consider four popular graph summary models on a large web graph. This resembles challenging multi-class vertex classification tasks with the numbers of classes ranging from $576$ to multiple hundreds of thousands. Our results show that the performance of GNNs are close to each other. In three out of four experiments, the non-message-passing GraphMLP model outperforms the other GNNs. The performance of the standard MLP is extraordinary good, especially in the presence of many classes. Finally, the Bloom filter outperforms all neural architectures by a large margin, except for the dataset with the fewest number of $576$ classes.

Related papers

• Spectral Greedy Coresets for Graph Neural Networks [61.24300262316091]
  The ubiquity of large-scale graphs in node-classification tasks hinders the real-world applications of Graph Neural Networks (GNNs) This paper studies graph coresets for GNNs and avoids the interdependence issue by selecting ego-graphs based on their spectral embeddings. Our spectral greedy graph coreset (SGGC) scales to graphs with millions of nodes, obviates the need for model pre-training, and applies to low-homophily graphs.
  arXiv  Detail & Related papers  (2024-05-27T17:52:12Z)
• A Topological characterisation of Weisfeiler-Leman equivalence classes [0.0]
  Graph Neural Networks (GNNs) are learning models aimed at processing graphs and signals on graphs. In this article, we rely on the theory of covering spaces to fully characterize the classes of graphs that GNNs cannot distinguish. We show that the number of indistinguishable graphs in our dataset grows super-exponentially with the number of nodes.
  arXiv  Detail & Related papers  (2022-06-23T17:28:55Z)
• Semi-Supervised Hierarchical Graph Classification [54.25165160435073]
  We study the node classification problem in the hierarchical graph where a 'node' is a graph instance. We propose the Hierarchical Graph Mutual Information (HGMI) and present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
  arXiv  Detail & Related papers  (2022-06-11T04:05:29Z)
• Exploiting Neighbor Effect: Conv-Agnostic GNNs Framework for Graphs with Heterophily [58.76759997223951]
  We propose a new metric based on von Neumann entropy to re-examine the heterophily problem of GNNs. We also propose a Conv-Agnostic GNN framework (CAGNNs) to enhance the performance of most GNNs on heterophily datasets.
  arXiv  Detail & Related papers  (2022-03-19T14:26:43Z)
• Incorporating Heterophily into Graph Neural Networks for Graph Classification [6.709862924279403]
  Graph Neural Networks (GNNs) often assume strong homophily for graph classification, seldom considering heterophily. We develop a novel GNN architecture called IHGNN (short for Incorporating Heterophily into Graph Neural Networks) We empirically validate IHGNN on various graph datasets and demonstrate that it outperforms the state-of-the-art GNNs for graph classification.
  arXiv  Detail & Related papers  (2022-03-15T06:48:35Z)
• 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)
• Imbalanced Graph Classification via Graph-of-Graph Neural Networks [16.589373163769853]
  Graph Neural Networks (GNNs) have achieved unprecedented success in learning graph representations to identify categorical labels of graphs. We introduce a novel framework, Graph-of-Graph Neural Networks (G$2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from graphs themselves. Our proposed G$2$GNN outperforms numerous baselines by roughly 5% in both F1-macro and F1-micro scores.
  arXiv  Detail & Related papers  (2021-12-01T02:25:47Z)
• AnchorGAE: General Data Clustering via $O(n)$ Bipartite Graph Convolution [79.44066256794187]
  We show how to convert a non-graph dataset into a graph by introducing the generative graph model, which is used to build graph convolution networks (GCNs) A bipartite graph constructed by anchors is updated dynamically to exploit the high-level information behind data. We theoretically prove that the simple update will lead to degeneration and a specific strategy is accordingly designed.
  arXiv  Detail & Related papers  (2021-11-12T07:08:13Z)
• Node Feature Extraction by Self-Supervised Multi-scale Neighborhood Prediction [123.20238648121445]
  We propose a new self-supervised learning framework, Graph Information Aided Node feature exTraction (GIANT) GIANT makes use of the eXtreme Multi-label Classification (XMC) formalism, which is crucial for fine-tuning the language model based on graph information. We demonstrate the superior performance of GIANT over the standard GNN pipeline on Open Graph Benchmark datasets.
  arXiv  Detail & Related papers  (2021-10-29T19:55:12Z)
• Scalable Graph Neural Networks for Heterogeneous Graphs [12.44278942365518]
  Graph neural networks (GNNs) are a popular class of parametric model for learning over graph-structured data. Recent work has argued that GNNs primarily use the graph for feature smoothing, and have shown competitive results on benchmark tasks. In this work, we ask whether these results can be extended to heterogeneous graphs, which encode multiple types of relationship between different entities.
  arXiv  Detail & Related papers  (2020-11-19T06:03:35Z)
• Few-Shot Learning on Graphs via Super-Classes based on Graph Spectral Measures [14.932318540666545]
  We study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples. We propose an approach where a probability measure is assigned to each graph based on the spectrum of the graphs normalized Laplacian.
  arXiv  Detail & Related papers  (2020-02-27T17:11:14Z)

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.