Approximate Network Motif Mining Via Graph Learning

• URL: http://arxiv.org/abs/2206.01008v1
• Date: Thu, 2 Jun 2022 12:15:05 GMT
• Title: Approximate Network Motif Mining Via Graph Learning
• Authors: Carlos Oliver, Dexiong Chen, Vincent Mallet, Pericles Philippopoulos, Karsten Borgwardt
• Abstract summary: Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. High computational complexity of identifying motif sets in arbitrary datasets has limited their use in many real-world datasets. By automatically leveraging statistical properties of datasets, machine learning approaches have shown promise in several tasks with complexity.
• Score: 4.2873412319680035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets. However, the high computational complexity of identifying motif sets in arbitrary datasets (motif mining) has limited their use in many real-world datasets. By automatically leveraging statistical properties of datasets, machine learning approaches have shown promise in several tasks with combinatorial complexity and are therefore a promising candidate for network motif mining. In this work we seek to facilitate the development of machine learning approaches aimed at motif mining. We propose a formulation of the motif mining problem as a node labelling task. In addition, we build benchmark datasets and evaluation metrics which test the ability of models to capture different aspects of motif discovery such as motif number, size, topology, and scarcity. Next, we propose MotiFiesta, a first attempt at solving this problem in a fully differentiable manner with promising results on challenging baselines. Finally, we demonstrate through MotiFiesta that this learning setting can be applied simultaneously to general-purpose data mining and interpretable feature extraction for graph classification tasks.

Related papers

• How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension [53.6373473053431]
  This work introduces a benchmark to assess large language models' capabilities in graph pattern tasks. We have developed a benchmark that evaluates whether LLMs can understand graph patterns based on either terminological or topological descriptions. Our benchmark encompasses both synthetic and real datasets, and a variety of models, with a total of 11 tasks and 7 models.
  arXiv  Detail & Related papers  (2024-10-04T04:48:33Z)
• Towards Graph Prompt Learning: A Survey and Beyond [38.55555996765227]
  Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability. This survey categorizes over 100 relevant works in this field, summarizing general design principles and the latest applications.
  arXiv  Detail & Related papers  (2024-08-26T06:36:42Z)
• SPGNN: Recognizing Salient Subgraph Patterns via Enhanced Graph Convolution and Pooling [25.555741218526464]
  Graph neural networks (GNNs) have revolutionized the field of machine learning on non-Euclidean data such as graphs and networks. We propose a concatenation-based graph convolution mechanism that injectively updates node representations. We also design a novel graph pooling module, called WL-SortPool, to learn important subgraph patterns in a deep-learning manner.
  arXiv  Detail & Related papers  (2024-04-21T13:11:59Z)
• End-to-End Learning on Multimodal Knowledge Graphs [0.0]
  We propose a multimodal message passing network which learns end-to-end from the structure of graphs. Our model uses dedicated (neural) encoders to naturally learn embeddings for node features belonging to five different types of modalities. Our results indicate that end-to-end multimodal learning from any arbitrary knowledge graph is indeed possible.
  arXiv  Detail & Related papers  (2023-09-03T13:16:18Z)
• MTS2Graph: Interpretable Multivariate Time Series Classification with Temporal Evolving Graphs [1.1756822700775666]
  We introduce a new framework for interpreting time series data by extracting and clustering the input representative patterns. We run experiments on eight datasets of the UCR/UEA archive, along with HAR and PAM datasets.
  arXiv  Detail & Related papers  (2023-06-06T16:24:27Z)
• Bures-Wasserstein Means of Graphs [60.42414991820453]
  We propose a novel framework for defining a graph mean via embeddings in the space of smooth graph signal distributions. By finding a mean in this embedding space, we can recover a mean graph that preserves structural information. We establish the existence and uniqueness of the novel graph mean, and provide an iterative algorithm for computing it.
  arXiv  Detail & Related papers  (2023-05-31T11:04:53Z)
• Temporal Graph Network Embedding with Causal Anonymous Walks Representations [54.05212871508062]
  We propose a novel approach for dynamic network representation learning based on Temporal Graph Network. For evaluation, we provide a benchmark pipeline for the evaluation of temporal network embeddings. We show the applicability and superior performance of our model in the real-world downstream graph machine learning task provided by one of the top European banks.
  arXiv  Detail & Related papers  (2021-08-19T15:39:52Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Learning the Implicit Semantic Representation on Graph-Structured Data [57.670106959061634]
  Existing representation learning methods in graph convolutional networks are mainly designed by describing the neighborhood of each node as a perceptual whole. We propose a Semantic Graph Convolutional Networks (SGCN) that explores the implicit semantics by learning latent semantic-paths in graphs.
  arXiv  Detail & Related papers  (2021-01-16T16:18:43Z)
• Graph Prototypical Networks for Few-shot Learning on Attributed Networks [72.31180045017835]
  We propose a graph meta-learning framework -- Graph Prototypical Networks (GPN) GPN is able to perform textitmeta-learning on an attributed network and derive a highly generalizable model for handling the target classification task.
  arXiv  Detail & Related papers  (2020-06-23T04:13:23Z)

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.