Graph Structure Learning from Unlabeled Data for Event Detection

01/05/2017
by   Sriram Somanchi, et al.
0

Processes such as disease propagation and information diffusion often spread over some latent network structure which must be learned from observation. Given a set of unlabeled training examples representing occurrences of an event type of interest (e.g., a disease outbreak), our goal is to learn a graph structure that can be used to accurately detect future events of that type. Motivated by new theoretical results on the consistency of constrained and unconstrained subset scans, we propose a novel framework for learning graph structure from unlabeled data by comparing the most anomalous subsets detected with and without the graph constraints. Our framework uses the mean normalized log-likelihood ratio score to measure the quality of a graph structure, and efficiently searches for the highest-scoring graph structure. Using simulated disease outbreaks injected into real-world Emergency Department data from Allegheny County, we show that our method learns a structure similar to the true underlying graph, but enables faster and more accurate detection.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/16/2021

Graph Structure Learning with Variational Information Bottleneck

Graph Neural Networks (GNNs) have shown promising results on a broad spe...
research
01/08/2020

Learning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes

Comorbid diseases co-occur and progress via complex temporal patterns th...
research
04/11/2022

Multi-view graph structure learning using subspace merging on Grassmann manifold

Many successful learning algorithms have been recently developed to repr...
research
11/18/2019

Graph estimation for Gaussian data zero-inflated by double truncation

We consider the problem of graph estimation in a zero-inflated Gaussian ...
research
08/14/2023

Graph Structural Residuals: A Learning Approach to Diagnosis

Traditional model-based diagnosis relies on constructing explicit system...
research
10/20/2017

On the Consistency of Graph-based Bayesian Learning and the Scalability of Sampling Algorithms

A popular approach to semi-supervised learning proceeds by endowing the ...
research
08/12/2020

Optimizing Graph Structure for Targeted Diffusion

The problem of diffusion control on networks has been extensively studie...

Please sign up or login with your details

Forgot password? Click here to reset