Changepoint Detection in Highly-Attributed Dynamic Graphs

• URL: http://arxiv.org/abs/2407.06998v1
• Date: Tue, 9 Jul 2024 16:12:44 GMT
• Title: Changepoint Detection in Highly-Attributed Dynamic Graphs
• Authors: Emiliano Penaloza, Nathaniel Stevens,
• Abstract summary: We leverage Graph Neural Networks to estimate each snapshot's modularity. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks. We find our method is able to detect a real-world event within the #Iran Twitter reply network.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting anomalous behavior in dynamic networks remains a constant challenge. This problem is further exacerbated when the underlying topology of these networks is affected by individual highly-dimensional node attributes. We address this issue by tracking a network's modularity as a proxy of its community structure. We leverage Graph Neural Networks (GNNs) to estimate each snapshot's modularity. GNNs can account for both network structure and high-dimensional node attributes, providing a comprehensive approach for estimating network statistics. Our method is validated through simulations that demonstrate its ability to detect changes in highly-attributed networks by analyzing shifts in modularity. Moreover, we find our method is able to detect a real-world event within the \#Iran Twitter reply network, where each node has high-dimensional textual attributes.

Related papers

• Formal Verification of Graph Convolutional Networks with Uncertain Node Features and Uncertain Graph Structure [7.133681867718039]
  Graph neural networks are becoming increasingly popular in the field of machine learning. They have been applied in safety-critical environments where perturbations inherently occur. This research addresses the non-passing gap by preserving the dependencies of all elements in the underlying computations.
  arXiv  Detail & Related papers  (2024-04-23T14:12:48Z)
• Degree-based stratification of nodes in Graph Neural Networks [66.17149106033126]
  We modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods.
  arXiv  Detail & Related papers  (2023-12-16T14:09:23Z)
• Graph Metanetworks for Processing Diverse Neural Architectures [33.686728709734105]
  Graph Metanetworks (GMNs) generalizes to neural architectures where competing methods struggle. We prove that GMNs are expressive and equivariant to parameter permutation symmetries that leave the input neural network functions.
  arXiv  Detail & Related papers  (2023-12-07T18:21:52Z)
• Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks [0.0]
  This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection. GNNs are a deep learning approach for graph-based data that incorporate graph structures into learning. We present Anomal-E, a GNN approach to intrusion and anomaly detection that leverages edge features and graph topological structure in a self-supervised process.
  arXiv  Detail & Related papers  (2022-07-14T10:59:39Z)
• Graph similarity learning for change-point detection in dynamic networks [15.694880385913534]
  We consider dynamic networks that are temporal sequences of graph snapshots. This task is often termed network change-point detection and has numerous applications, such as fraud detection or physical motion monitoring. We design a method to perform online network change-point detection that can adapt to the specific network domain and localise changes with no delay.
  arXiv  Detail & Related papers  (2022-03-29T12:16:38Z)
• 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)
• Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks [78.65792427542672]
  Dynamic Graph Network (DG-Net) is a complete directed acyclic graph, where the nodes represent convolutional blocks and the edges represent connection paths. Instead of using the same path of the network, DG-Net aggregates features dynamically in each node, which allows the network to have more representation ability.
  arXiv  Detail & Related papers  (2020-10-02T16:50:26Z)
• 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)
• Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs [54.13919050090926]
  We propose an end-to-end structural temporal Graph Neural Network model for detecting anomalous edges in dynamic graphs. In particular, we first extract the $h$-hop enclosing subgraph centered on the target edge and propose the node labeling function to identify the role of each node in the subgraph. Based on the extracted features, we utilize Gated recurrent units (GRUs) to capture the temporal information for anomaly detection.
  arXiv  Detail & Related papers  (2020-05-15T09:17:08Z)
• Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks [183.97265247061847]
  We leverage graph signal processing to characterize the representation space of graph neural networks (GNNs) We discuss the role of graph convolutional filters in GNNs and show that any architecture built with such filters has the fundamental properties of permutation equivariance and stability to changes in the topology. We also study the use of GNNs in recommender systems and learning decentralized controllers for robot swarms.
  arXiv  Detail & Related papers  (2020-03-08T13:02:15Z)

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.