Automated weak signal detection and prediction using keyword network clustering and graph convolutional network
Bibliographic Data
| ID | 9844431 |
|---|---|
| Authors | Taehyun Ha (0000-0003-3143-666X, Korean Institute of Architects), Heyoung Yang (0000-0002-7960-4389, Korean Institute of Architects, corresponding author), Sungwha Hong, Sung-Wha Hong (Korea Institute of Science & Technology Information) |
| Year | 2023 |
| Volume | 152 |
| Pages | 103202 |
| Publication date | 2023-09-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Futures (JOURNAL) |
| Journal identifiers | ISSN: 0016-3287 • E-ISSN: 1873-6378 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.futures.2023.103202 |
| OpenAlex | W4381805336 |
| Language | EN |
| Citations received | 3 |
| References cited | 25 |
Weak signals are rarely identified in the initial stage of growth and appear significant over time, unlike strong signals clearly observed in past trends. Weak signals are important cues that need to be analyzed to rapidly and accurately predict changes in the uncertain future. Researchers have developed various methods for identifying cues that can be significantly used for prediction. However, in many cases, they heavily depend on the opinions of experts or are applicable only to weak signals in specific fields. This study proposes a weak signal detection method that extracts weak signals by selecting significant keywords from literature database and grouping relevant keywords. Furthermore, this study presents a weak signal prediction method for predicting the growth of specific weak signals by investigating and learning the growth of the extracted weak signals over 10 years. To verify the proposed method, we extracted weak signals for 10 years (2001–2010) from SCOPUS publication data from 1996 to 2009 and applied machine learning using a graph convolutional network (GCN) model with the growth data of the extracted weak signals. The results showed that the proposed methods can effectively detect and predict weak signals
Cluster analysis · Data mining · Graph · Machine learning · Pattern recognition (psychology · SIGNAL (programming language · Advanced Text Analysis Techniques · Artificial Intelligence · Cognitive Science and Mapping · Computer Science · Grey System Theory Applications · Theoretical Computer Science
Managing Strategic Surprise by Response to Weak Signals
Forecasting emerging technologies using data augmentation and deep learning
Unifying weak signals definitions to improve construct understanding
Small seeds for grand challenges—Exploring disregarded seeds of change in a foresight process for RTI policy
Using a conceptual system for weak signals classification to detect threats and opportunities from web
Weak signals
| Unique citing works | 3 |
|---|---|
| Citations per year | 3 |
| Citation span | 2025 - 2026 (2) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 3 |