面对空间社会网络、交通网络等位置感知网络数据日益增长的挑战，网络数据处理技术在数据收集、清理、组织、解释、分析、利用和可视化等各个阶段都经历着革命性的变化。这些变化导致大数据框架、网络分析建模、链接或路径预测、推荐系统趋同成为全球瞩目的发展趋势。本特刊旨在提供一个论坛，介绍关于大型复杂网络数据的融合研究的最新进展。挑战包括城市中的实时事件检测、交通网络中的拥堵发现、社交用户的位置预测、物理世界中的社交用户行为识别以及处理多维复杂网络数据的统一系统。稳健的解决方案要求在机器学习、遗传算法、混沌、遗传算法、细胞自动机、神经网络和进化博弈论等领域采用高度创新的技术。

特别问题吸引了全球研究人员在机器学习，人工智能，数据挖掘，自然语言处理，数据和网站挖掘以及大数据管理方面的高质量提交，以及利用其专业知识和匹配发展挑战的大数据管理更高效和实用的算法或模型，以获得每日生成无价的社交网络数据和业务网络数据的智能知识。提交的总计是40.在至少两个审稿人的单盲同行评审后，最终被接受了19篇论文。接受的率是47.5％。每个接受纸张的平均作者数量为4.2。附属机构来自中国，澳大利亚，法国，印度，沙特阿拉伯和加拿大。这些已接受的论文可以在不同的群体中组织。第一组文章的重点是基于位置的社交网络研究。S. Yang等人标题为“目标感知节点选择的目标有影响的节点选择”。提出了一种有效的解决方案，可以在位置感知社交网络中识别基于目标的有影响力的节点。 The paper titled “Discovering Travel Community for POI Recommendation on Location-Based Social Networks” by L. Tang et al. improved the community detection method for high-quality point-of-interest recommendation. The other three papers—“A Joint Deep Recommendation Framework for Location-Based Social Networks” by O. Tal and Y. Liu, “Optimal Proxy Selection for Socioeconomic Status Inference on Twitter” by J. L. Abitbol et al., and “Incremental Bilateral Preference Stable Planning over Event Based Social Networks” by B. Li et al.—invented the new machine learning technologies to explore the useful annotations for users and recommendation. The focus of the second group of articles is traffic based road network research. The papers titled “An Effective Algorithm for Video-Based Parking and Drop Event Detection” by G. Li et al., “Location-Aware Web Service Composition Based on the Mixture Rank of Web Services and Web Service Requests” by J. Lu et al., and “Predicting Quality of Service via Leveraging Location Information” by L. Chen et al. provided the effective solution to discover the important location of delivering high-quality service in smart city environment. The other three papers titled “Semantic-Aware Top-k Multirequest Optimal Route” by S. Wang et al., “A Novel Index Method for K Nearest Object Query over Time-Dependent Road Networks” by Y. Yang et al., and “A Destination Prediction Network Based on Spatiotemporal Data for Bike-Sharing” by J. Jiang et al. proposed efficient algorithms and models to choose the optimal route planning. The focus of the third group of articles is deep learning. The papers titled “Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning” by W. Yuan et al., “Cognitive Driven Multilayer Self-Paced Learning with Misclassified Samples” by Q. Zhu et al., and “A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles” by Q. Meng et al. investigated the deep learning technologies to optimize the entity recognition in social network data. The other five papers titled “pSPARQL: A Querying Language for Probabilistic RDF Data” by H. Fang, “Edge Computing in an IoT Base Station System: Reprogramming and Real-Time Tasks” by H. Wu et al., “Finding the Shortest Path with Vertex Constraint over Large Graphs” by Y. Yang et al., “Evaluation of Residential Housing Prices on the Internet: Data Pitfalls” by M. Li et al., and “Promoting Geospatial Service from Information to Knowledge with Spatiotemporal Semantics” by J. Geng et al. contributed a diverse range of topics using the semantic information. The solutions proposed in these research works include transfer learning, edge computing, data index structure, self-space learning, video analysis, feature fusion networks, multilayer classification, spatiotemporal pattern recognition, and probabilistic resource description framework data analysis. The effectiveness of the proposed solutions to the targeted problems has been reported based on empirical study and/or analysis.

综上所述，研究论文涵盖了广泛的应用领域，包括地理空间服务、基于位置的社会网络推荐、社会网络影响节点选择、住宅房价预测的数据陷阱检测、社会经济状况推断、停车检测、自动驾驶汽车目标检测、服务质量预测、共享单车、社交网络标签预测、时变道路网络k近邻查询、物联网基站系统优化。