Electric vehicles (EVs) are a sustainable transportation solution with environmental benefits and energy efficiency. However, their popularity has raised challenges in locating appropriate charging stations, especially in cities with limited infrastructure and dynamic charging demands. To address this, we propose a multi-agent deep deterministic policy gradient (MADDPG) method for optimal EV charging station recommendations, considering real-time traffic conditions. Our approach aims to minimize total travel time in a stochastic environment for efficient smart transportation management. We adopt a centralized learning and decentralized execution strategy, treating each region of charging stations as an individual agent. Agents cooperate to recommend optimal charging stations based on various incentive functions and competitive contexts. The problem is modeled as a Markov game, suitable for analyzing multi-agent decisions in stochastic environments. Intelligent transportation systems provide us with traffic information, and each charging station feeds relevant data to the agents. Our MADDPG method is challenged with a substantial number of EV requests, enabling efficient handling of dynamic charging demands. Simulation experiments compare our method with DDPG and deterministic approaches, considering different distributions and EV numbers. The results highlight MADDPG’s superiority, emphasizing its value for sustainable urban mobility and efficient EV charging station scheduling.

Multimodal learning heterogeneous graphs are very challenging because of the diverse structures and data modalities. The existing graph neural networks cannot efficiently capture both the multimodality of the data and the inherent heterogeneity of such graphs. In this paper, we propose Multimodal Representation Learning Heterogeneous Graph Neural network (MM-HGNN) to tackle these challenges. MM-HGNN introduces a novel Modality Transferability Function to quantify the heterogeneity between different modalities, which allows the model to dynamically adjust the attention scores and give precedence to unique information that is non-redundant. Additionally, it integrates modality-level attention that distributes attention in an adaptive way over different modalities according to their relevance, enhancing feature representations for tasks such as node classification. To further improve representation learning, a splicing mechanism is proposed to integrate outputs from multiple network layers, combining high-level features for more expressive node embeddings. We validate the effectiveness of MM-HGNN through extensive experiments on the IMDB and Amazon datasets. Our model outperforms several state-of-the-art methods under the Macro-F1, Micro-F1, and AUC metrics by a large margin, which well demonstrates its strong capability in dealing with the challenging multimodal and heterogeneous data. Comprehensive ablation studies further emphasize the contributions of each key component in improving the overall performance.

Recently, an increasing body of work investigates networks with multiple types of links. Variants of such systems have been examined decades ago in disciplines such as sociology and engineering, but only recently have they been unified within the framework of multilayer networks. In parallel, many aspects of real systems are increasingly and routinely sensed, measured and described, resulting in many private, but also open data sets. In many domains publicly available repositories of open data sets constitute a great opportunity for domain experts to contextualise their privately generated data compared to publicly available data in their domain. We propose in this paper a methodology for multilayer network analysis in order to provide domain experts with measures and methods to understand, evaluate and complete their private data by comparing and/or combining them with open data when both are modelled as multilayer networks. We illustrate our methodology through a biological application where interactions between molecules are extracted from open databases and modelled by a multilayer network and where private data are collected experimentally. This methodology helps biologists to compare their private networks with the open data, to assess the connectivity between the molecules across layers and to compute the distribution of the identified molecules in the open network. In addition, the shortest paths which are biologically meaningful are also analysed and classified.