Graph Neural Networks and Learning Systems

Topic Information

Dear Colleagues,

Learning with graph-structured data, such as molecular, social, biological, and financial networks, requires effective representations of their graph structure. Recently, there has been a surge of interest in graph neural network (GNN) approaches for representation learning of graphs. GNNs typically follow a recursive neighborhood aggregation scheme, where each node aggregates feature vectors of its neighbors to compute its new features. Empirically, GNNs have achieved state-of-the-art performance in many tasks such as learning system function, node classification, link prediction, and graph classification.

The Topic “Graph Neural Networks and Learning Systems” aims to attract cutting-edge research in this fascinating domain. Historically, GNNs have yielded groundbreaking progress in tackling real-world challenges, from anomaly detection to recommender systems, traffic forecasting, disease control, and drug discovery. Despite their rapid emergence and success, the field faces challenges in areas such as fundamental theory and models, algorithms and methods, supporting tools and platforms, and real-world applications. As GNNs have enormous potential applications, this topic is both exciting and controversial. Please join us in creating a diverse collection of articles, and we look forward to receiving your contributions.

Prof. Dr. Huijia Li
Dr. Jun Hu
Dr. Weichen Zhao
Prof. Dr. Jie Cao
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Computers computers	4.2	7.5	2012	16.3 Days	CHF 1800	Submit
Information information	2.9	6.5	2010	18.6 Days	CHF 1800	Submit
AI ai	5.0	6.9	2020	20.7 Days	CHF 1600	Submit
Electronics electronics	2.6	6.1	2012	16.8 Days	CHF 2400	Submit
Technologies technologies	3.6	8.5	2013	21.8 Days	CHF 1600	Submit
Big Data and Cognitive Computing BDCC	4.4	9.8	2017	24.5 Days	CHF 1800	Submit

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Published Papers (1 paper)

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All Computers Information AI Electronics Technologies BDCC

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19 pages, 1247 KiB  

Open AccessArticle

Improving News Retrieval with a Learnable Alignment Module for Multimodal Text–Image Matching

by Rui Song, Jiwei Tian, Peican Zhu and Bin Chen

Electronics 2025, 14(15), 3098; https://doi.org/10.3390/electronics14153098 - 3 Aug 2025

Viewed by 438

Abstract

With the diversification of information retrieval methods, news retrieval tasks have gradually evolved towards multimodal retrieval. Existing methods often encounter issues such as inaccurate alignment and unstable feature matching when handling cross-modal data like text and images, limiting retrieval performance. To address this, [...] Read more.

With the diversification of information retrieval methods, news retrieval tasks have gradually evolved towards multimodal retrieval. Existing methods often encounter issues such as inaccurate alignment and unstable feature matching when handling cross-modal data like text and images, limiting retrieval performance. To address this, this paper proposes an innovative multimodal news retrieval method by introducing the Learnable Alignment Module (LAM), which establishes a learnable alignment relationship between text and images to improve the accuracy and stability of cross-modal retrieval. Specifically, the LAM, through trainable label embeddings (TLEs), enables the text encoder to dynamically adjust category information during training, thereby enhancing the alignment capability of text and images in the shared embedding space. Additionally, we propose three key alignment strategies: logits calibration, parameter consistency, and semantic feature matching, to further optimize the model’s multimodal learning ability. Extensive experiments conducted on four public datasets—Visual News, MMED, N24News, and EDIS—demonstrate that the proposed method outperforms existing state-of-the-art approaches in both text and image retrieval tasks. Notably, the method achieves significant improvements in low-recall scenarios (R@1): for text retrieval, R@1 reaches 47.34, 44.94, 16.47, and 19.23, respectively; for image retrieval, R@1 achieves 40.30, 38.49, 9.86, and 17.95, validating the effectiveness and robustness of the proposed method in multimodal news retrieval. Full article

(This article belongs to the Topic Graph Neural Networks and Learning Systems)

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