Influence Maximization (IM) is a critical task in social network analysis, yet its application to large-scale networks is often hampered by computational complexity and the challenge of effectively identifying influential communities. Existing community-based IM algorithms frequently rely on traditional detection methods that may not scale well or adequately capture influence propagation dynamics. While deep learning has advanced community detection, the explicit integration of influence properties into these models remains a significant gap. This paper introduces the Deep Learning-based Community-aware Influence Maximization (DLCIM) algorithm, a novel approach that synergistically combines deep learning for community detection with a tailored focus on influence diffusion characteristics. DLCIM employs an autoencoder framework with a novel influence-aware modularity maximization objective to learn node representations that are sensitive to information propagation. Subsequently, it filters important communities and allocates seed quotas proportionally, followed by efficient seed selection using established techniques within these communities. Comprehensive experiments on real-world datasets demonstrate that DLCIM achieves superior influence spread and competitive computational efficiency compared to state-of-the-art baseline algorithms.

Knowledge Graphs (KGs) have emerged as powerful tools for organizing and representing structured knowledge, enabling advanced reasoning and intelligent applications across diverse domains. Traditional approaches to Knowledge Graph Construction (KGC) rely on rule-based systems or supervised learning methods that require extensive feature engineering and labeled data, often limiting their generalization capabilities. Recently, large language models (LLMs) have shown great promise in improving KGC tasks by offering contextualized representations that enhance entity and relation extraction from unstructured text. However, one major limitation of LLMs is their tendency to generate hallucinated or factually incorrect information, which undermines the reliability of the resulting knowledge graphs. To address this challenge, we propose RLRSKGC (Reinforcement Learning with Reward Shaping for Knowledge Graph Construction), a novel framework that integrates reinforcement learning (RL) with Transformer-based language models to improve factual consistency and reduce hallucinations in the knowledge extraction process. Our approach formulates KGC as a sequential decision-making problem and introduces reward shaping mechanisms that explicitly evaluate the accuracy, completeness, and structural coherence of the generated knowledge. We conduct comprehensive experiments on benchmark KGC datasets to validate the effectiveness of our framework, demonstrating that RLRS-KGC achieves superior performance in extracting high-quality, graph-structured knowledge from textual sources.

Telecom fraud in mobile communication networks has become a serious threat to user security and network integrity. Traditional graph neural networks (GNNs) struggle to effectively detect fraudulent activities due to the pervasive noise in real-world fraud data, where genuine fraud signals are often obscured by spurious interactions and feature corruption. To address this challenge, we propose a novel framework combining a Top-p Neighbor Sampler and an adaptive graph neural network module, which selectively aggregates reliable neighbor features while suppressing noise propagation. Experiments on a real-world telecom fraud dataset demonstrate that our model outperforms state-of-the-art methods in macro-F1, AUC, and recall for fraud detection. This work not only provides a practical solution for telecom fraud detection but also offers insights into handling noise contamination in graph-structured data.

The essence of Influence Maximization (IM) lies in determining seed nodes that maximize influence under specific diffusion models. As social networks become increasingly vast and complex, the rapid and efficient identification of seed nodes for information diffusion has become a research priority. The reward mechanisms in Deep Reinforcement Learning (DRL) naturally align with the methods of selecting seed nodes based on the increment of the influence spread in the IM problem. Consequently, this alignment has attracted significant research attention and inspired various methodological innovations. Despite advancements in optimization techniques, significant challenges remain, particularly in terms of inadequate node feature embedding and value function overestimation. To address these limitations, we propose DKIM, a novel model integrating node embedding with DRL. This model incorporates Kshell positional encoding for effective node representation and employs a Multi-DQN algorithm designed with multi-target networks and adversarial loss (ALoss) function to enhance training efficiency. These innovations effectively mitigate boundary node effects and Q-value overestimation issues. We conduct comprehensive experiments comparing DKIM with state-of-the-art algorithms across four public datasets—Wiki-vote, LastFM-Asia, P2P-Gnutella08, and Facebook. The results demonstrate that DKIM achieves optimal performance on most IM tasks, providing a viable approach for RL-based models in addressing IM problems.

Poor coverage quality is a common cause of poor wireless communication network quality, which seriously affects the user experience in mobile communication. Currently, the front line mainly adopts a manual trial-and-error method, which has problems such as low efficiency and high human cost. How to use artificial intelligence algorithms to quickly and accurately identify and solve the problem of poor coverage quality based on existing data is one of the important research directions in the field of wireless networks. The data of wireless networks is essentially spatio-temporal data, but most of the existing methods are based on time-domain and space-domain data for analysis and modeling, and the information mining in the spatio domain is not sufficient. In the spatio domain, the distribution of base stations is not uniform in Euclidean space, which increases the difficulty of spatio-temporal modeling. In view of the natural advantages of graph mining technology for modeling and processing unstructured data, this paper proposes a model named Spatio-Temporal Graph Message Propagation (STGMP) based on graph technology. This method uses spatio-temporal graphs to represent the historical states of related service cells, proposes a processing layer that combines the time and spatio domains, and maps the actual problem to a multi-classification task, thereby achieving the identification of the causes of poor coverage quality. This paper also conducts experiments on real data sets, and the results show that the proposed method STGMP is very effective.

The next-generation network (6G) has more strict requirements for the online learning ability and high interpretability of the learned systems. Machine learning is expected to be essential to assist in making the networks efficient and adaptable, but most promising methods often are treated as “black boxes” due to the deep structures and high nonlinearity. Therefore, this article attempts to study the interpretations of machine learning algorithms to make them more applicable to the 6G-enabled Internet of things (IoT) networks. Typically, this article focuses on the new model GBDT2NN, which distills the knowledge learned by gradient boosting decision tree (GBDT) into neural network (NN) models to retain the learning ability of numerical data and rise the ability of online learning at the same time, but it loses the interpretability. This article conducts an empirical study on explaining individual prediction of GBDT2NN by taking use of the feature importance learned from GBDT, and then further explores whether the explanation can improve the approximation process. In addition, this article proposes two methods to obtain the interpretations: 1) the independent method and 2) the joint method. The experiments on several data sets of IoT networks show that the proposed methods can achieve better performance on both explanations and predictions.