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
- 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) - Affinity-Aware Graph Networks [9.888383815189176]
Graph Neural Networks (GNNs) have emerged as a powerful technique for learning on relational data.
We explore the use of affinity measures as features in graph neural networks.
We propose message passing networks based on these features and evaluate their performance on a variety of node and graph property prediction tasks.
arXiv Detail & Related papers (2022-06-23T18:51:35Z) - 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) - Deep Reinforcement Learning of Graph Matching [63.469961545293756]
Graph matching (GM) under node and pairwise constraints has been a building block in areas from optimization to computer vision.
We present a reinforcement learning solver for GM i.e. RGM that seeks the node correspondence between pairwise graphs.
Our method differs from the previous deep graph matching model in the sense that they are focused on the front-end feature extraction and affinity function learning.
arXiv Detail & Related papers (2020-12-16T13:48:48Z) - 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.