Discovering Communication Pattern Shifts in Large-Scale Labeled Networks
using Encoder Embedding and Vertex Dynamics
- URL: http://arxiv.org/abs/2305.02381v2
- Date: Wed, 29 Nov 2023 21:05:22 GMT
- Title: Discovering Communication Pattern Shifts in Large-Scale Labeled Networks
using Encoder Embedding and Vertex Dynamics
- Authors: Cencheng Shen, Jonathan Larson, Ha Trinh, Xihan Qin, Youngser Park,
Carey E. Priebe
- Abstract summary: Analyzing large-scale time-series network data, such as social media and email communications, poses a significant challenge.
We introduce a temporal encoder embedding method, enabling efficient embedding of large-scale graph data.
We demonstrate our approach by analyzing an anonymized time-series communication network from a large organization.
- Score: 15.672962997741385
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Analyzing large-scale time-series network data, such as social media and
email communications, poses a significant challenge in understanding social
dynamics, detecting anomalies, and predicting trends. In particular, the
scalability of graph analysis is a critical hurdle impeding progress in
large-scale downstream inference. To address this challenge, we introduce a
temporal encoder embedding method. This approach leverages ground-truth or
estimated vertex labels, enabling an efficient embedding of large-scale graph
data and the processing of billions of edges within minutes. Furthermore, this
embedding unveils a temporal dynamic statistic capable of detecting
communication pattern shifts across all levels, ranging from individual
vertices to vertex communities and the overall graph structure. We provide
theoretical support to confirm its soundness under random graph models, and
demonstrate its numerical advantages in capturing evolving communities and
identifying outliers. Finally, we showcase the practical application of our
approach by analyzing an anonymized time-series communication network from a
large organization spanning 2019-2020, enabling us to assess the impact of
Covid-19 on workplace communication patterns.
Related papers
- SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting [9.013416216828361]
We present a Series-Aligned Multi-Scale Graph Learning (SGL) framework, aiming to enhance forecasting performance.
In this work, we propose a series-aligned graph layer to facilitate the aggregation of non-delayed graph signals.
We conduct experiments on meteorological and traffic forecasting datasets, which demonstrate its effectiveness and superiority.
arXiv Detail & Related papers (2023-12-05T10:37:54Z) - Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings.
We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs.
Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
arXiv Detail & Related papers (2023-10-05T02:58:29Z) - Coupled Attention Networks for Multivariate Time Series Anomaly
Detection [10.620044922371177]
We propose a coupled attention-based neural network framework (CAN) for anomaly detection in multivariate time series data.
To capture inter-sensor relationships and temporal dependencies, a convolutional neural network based on the global-local graph is integrated with a temporal self-attention module.
arXiv Detail & Related papers (2023-06-12T13:42:56Z) - Graph Neural Networks for Multi-Robot Active Information Acquisition [15.900385823366117]
A team of mobile robots, communicating through an underlying graph, estimates a hidden state expressing a phenomenon of interest.
Existing approaches are either not scalable, unable to handle dynamic phenomena or not robust to changes in the communication graph.
We propose an Information-aware Graph Block Network (I-GBNet) that aggregates information over the graph representation and provides sequential-decision making in a distributed manner.
arXiv Detail & Related papers (2022-09-24T21:45:06Z) - Dynamic Graph Message Passing Networks for Visual Recognition [112.49513303433606]
Modelling long-range dependencies is critical for scene understanding tasks in computer vision.
A fully-connected graph is beneficial for such modelling, but its computational overhead is prohibitive.
We propose a dynamic graph message passing network, that significantly reduces the computational complexity.
arXiv Detail & Related papers (2022-09-20T14:41:37Z) - Dynamic Community Detection via Adversarial Temporal Graph
Representation Learning [17.487265170798974]
In this work, an adversarial temporal graph representation learning framework is proposed to detect dynamic communities from a small sample of brain network data.
In addition, the framework employs adversarial training to guide the learning of temporal graph representation and optimize the measurable modularity loss to maximize the modularity of community.
arXiv Detail & Related papers (2022-06-29T08:44:22Z) - From Unsupervised to Few-shot Graph Anomaly Detection: A Multi-scale
Contrastive Learning Approach [49.439021563395976]
Anomaly detection from graph data is an important data mining task in many applications such as social networks, finance, and e-commerce.
We propose a novel framework, graph ANomaly dEtection framework with Multi-scale cONtrastive lEarning (ANEMONE in short)
By using a graph neural network as a backbone to encode the information from multiple graph scales (views), we learn better representation for nodes in a graph.
arXiv Detail & Related papers (2022-02-11T09:45:11Z) - Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training.
FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
arXiv Detail & Related papers (2020-10-19T21:51:47Z) - Towards Deeper Graph Neural Networks [63.46470695525957]
Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations.
Several recent studies attribute this performance deterioration to the over-smoothing issue.
We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
arXiv Detail & Related papers (2020-07-18T01:11:14Z) - 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) - Understanding Crowd Behaviors in a Social Event by Passive WiFi Sensing
and Data Mining [21.343209622186606]
We propose a comprehensive data analysis framework to extract three types of patterns related to crowd behaviors in a large social event.
First, trajectories of the mobile devices are extracted from probe requests to reveal the spatial patterns of the crowds' movement.
Next, k-means and k-shape clustering algorithms are applied to extract temporal patterns visiting the crowds by days and locations.
arXiv Detail & Related papers (2020-02-05T03:36:00Z)
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.