Mengran Li
Biographic Data
| ID | 4464245 |
|---|---|
| NAME | Mengran Li |
| GIVEN NAMES | Mengran |
| FAMILY NAME | Li |
| SIGNATURE | LI M |
| AFFILIATIONS | Sun Yat-sen University |
| ORCID | 0000-0001-7858-0533 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 5 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
TDG-Mamba: Advanced Spatiotemporal Embedding for Temporal Dynamic Graph Learning via Bidirectional Information Propagation
Temporal dynamic graphs (TDGs), representing the dynamic evolution of entities and their relationships over time with intricate temporal features, are widely used in various real-world domains. Existing methods typically rely on mainstream techniques such as transformers and graph neural networks (GNNs) to capture the spatiotemporal information of TDGs. However, despite their advanced capabilities, these methods often struggle with significant co…
Determinants and strategies of food security in smallholders' homegardens in Xiaoliangshan, SW China
This study examines the determinants of food security in smallholders’ homegardens in Xiaoliangshan, southwest China, through a mixed-methods approach combining field surveys, interviews, and spatial-statistical analyses. Key findings reveal that management time significantly enhances food security (explanatory power: 10.4 %, p < 0.01), suggesting that increased labor investment in homegardens directly improves household food availability. Simila…
Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user–item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dyn…
Csat: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks
Community detection aims to identify dense subgroups of nodes within a network. However, in real-world networks, node attributes are often missing, making traditional methods less effective. In networks with missing attributes, the main challenge of community detection is to deal with the missing attribute information efficiently and use network structure information to make accurate predictions. This article proposes an innovative method called …
Mismatches in Suppliers’ and Demanders’ Cognition, Willingness and Behavior with Respect to Ecological Protection of Cultivated Land: Evidence from Caidian District, Wuhan, China
Cultivated land systems have an enormous ecological function value with respect to water conversation, nutrient circulation and climate regulation. The people's cognition, willingness and behavior may prove to be pivotal in ecologically protecting cultivated land. The purpose of this paper is to explore suppliers' and demanders' cognition, willingness and behavior with respect to the ecological protection of cultivated land. The second-order stru…
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Mismatches in Suppliers’ and Demanders’ Cognition, Willingness and Behavior with Respect to Ecological Protection of Cultivated Land: Evidence from Caidian District, Wuhan, China
Cultivated land systems have an enormous ecological function value with respect to water conversation, nutrient circulation and climate regulation. The people's cognition, willingness and behavior may prove to be pivotal in ecologically protecting cultivated land. The purpose of this paper is to explore suppliers' and demanders' cognition, willingness and behavior with respect to the ecological protection of cultivated land. The second-order stru…
Contextual Semantics Interaction Graph Embedding Learning for Recommender Systems
Recommender systems have become an indispensable tool in today's digital age, significantly enhancing user engagement on various online platforms by curating personalized item recommendations tailored to individual preferences. While the field has long been dominated by the collaborative filtering technique, which primarily leverages user–item interaction data, it often falls short in encapsulating the rich contextual intricacies and evolving dyn…
Csat: Contrastive Sampling-Aggregating Transformer for Community Detection in Attribute-Missing Networks
Community detection aims to identify dense subgroups of nodes within a network. However, in real-world networks, node attributes are often missing, making traditional methods less effective. In networks with missing attributes, the main challenge of community detection is to deal with the missing attribute information efficiently and use network structure information to make accurate predictions. This article proposes an innovative method called …
TDG-Mamba: Advanced Spatiotemporal Embedding for Temporal Dynamic Graph Learning via Bidirectional Information Propagation
Temporal dynamic graphs (TDGs), representing the dynamic evolution of entities and their relationships over time with intricate temporal features, are widely used in various real-world domains. Existing methods typically rely on mainstream techniques such as transformers and graph neural networks (GNNs) to capture the spatiotemporal information of TDGs. However, despite their advanced capabilities, these methods often struggle with significant co…
Determinants and strategies of food security in smallholders' homegardens in Xiaoliangshan, SW China
This study examines the determinants of food security in smallholders’ homegardens in Xiaoliangshan, southwest China, through a mixed-methods approach combining field surveys, interviews, and spatial-statistical analyses. Key findings reveal that management time significantly enhances food security (explanatory power: 10.4 %, p < 0.01), suggesting that increased labor investment in homegardens directly improves household food availability. Simila…
Advanced Graph Neural Networks (3 works) · Artificial Intelligence (3 works) · Computer Science (3 works) · Agriculture (2 works) · Business (2 works) · China (2 works) · Economics (2 works) · Embedding (2 works) · Geography (2 works) · Graph (2 works)