Mobile devices’ multiple network interfaces create the foundation for multipath optimization, yet widespread implementation remains challenging. Existing multipath solutions primarily rely on simulation environments or require hardware/software adaptations, limiting real-world adoption. Meanwhile, 89.8% of mobile users prefer WiFi over cellular networks due to data cost concerns, despite cellular networks often providing better stability. We propose WiCell, an application-transparent multipath enhancement system for mobile networks that addresses these challenges through: (1) a deployment-friendly approach requiring no hardware or software modifications; (2) a tunneling-based architecture with edge proxy servers that dynamically adjusts transmission based on real-time link condition feedback; and (3) two operational modes tailored to different user requirements - WiFi-Primary Adaptive Redundancy Mode for balanced performance and data conservation, and Dual Transmission Mode for maximum connection stability. Our comprehensive evaluation demonstrates WiCell’s effectiveness across 30 common applications. In WiFi-Primary Adaptive Redundancy Mode, WiCell achieves a 74.6% reduction in video stalling rate while consuming only 22.12% of the cellular data compared to full redundancy approaches. In Dual Transmission Mode, WiCell reduces maximum latency by 77.4% compared to WiFi-only transmission, with 61.36% lower average latency than WiFi and 65.84% lower than cellular-only connections. These results validate WiCell as a practical solution for enhancing wireless network performance while maintaining application transparency and addressing user concerns about cellular data consumption.

In recent years, knowledge graph (KG) has obtained many achievements in both research and industrial fields. However, most KG algorithms consider node embedding with only structure and node features, but not relation features. In this paper, we propose a novel Heterogeneous Attention (HAT) algorithm and use both node-based and path-based attention mechanisms to learn various types of nodes and edges on the KG. To better capture representations, multi-hop relation features are involved to generate edge embeddings and help the model obtain more semantic information. To capture a more complex representation, we design different encoder parameters for different types of nodes and edges in HAT. Extensive experiments validate that our HAT significantly outperforms the state-of-the-art methods on both the public datasets and a large-scale real-world fintech dataset1.

A fundamental problem encountered in designing decentralized social applications is the issue of efficiently locating target nodes in social peer-to-peer networks based on local information only. The unique “small-world” phenomenon of social networks shows that a typical pair of nodes is connected by very short chains of intermediate friends, and individuals are able to collectively discover such short paths. In this paper, we propose a decentralized algorithm that exploits this “small-world” phenomenon to discover efficient routes to reach target nodes in unstructured social overlays. In order to test the performance of this algorithm, we simulate it on a real-world social graph dataset crawled from a large online social networking website (LiveJournal). Compared with other related works, the simulation results show that our proposed decentralized social routing algorithm not only shortens the median length of the search path, but also increases the success rate of finding such routes between arbitrary pair of nodes in the LiveJournal social graph.