Contrastive learning has received significant attention for its ability to improve the representation quality over limited labeled data, achieving notable advancements in Knowledge graphs (KGs) enhanced recommendation. However, the effectiveness of most contrastive methods heavily relies on manually designed data augmentation strategies, which often limit their generality across different datasets and downstream tasks, as well as their robustness to noise perturbations. To address these challenges, we propose a novel KG-enhanced recommendation framework namedKnowledgeGraph-enhancedMaskedAuto-Encoders forCollaborative Filtering (KG-CMAE) based on the masking-reconstruction paradigm, which employs two types of masked auto-encoders: the Knowledge Masked Auto-Encoder (KMAE) and the Collaborative Masked Auto-Encoder (CMAE), to adaptively extract informative self-supervised signals from KGs and user-item interactions. Specifically, KMAE employs the multi-head cross-attention mechanism to reflect the importance of neighboring nodes, and selectively masks and reconstructs the important connections with high structural consistency, thereby highlighting task-relevant knowledge. CMAE focuses on masking and reconstructing the interactions with high semantic relevance in the user-item interaction graph, and incorporates an enhanced decoder to better model the direct collaborative signals, such as user-user and item-item correlations, to mitigate the over-smoothing problem. Extensive experiments are conducted on three benchmark datasets under various settings, including noisy data, cold-start user recommendation, and long-tail item recommendation. The experimental results demonstrate the effectiveness, generality and robustness of the proposed KG-CMAE model compared to various baseline methods.

Rumors can spread very rapidly through online social networks (OSNs), leading to huge negative impact on human society. Hence, there is an urgent need to develop models that can minimize the spread of rumors. In this article, we propose a novel framework to improve the cost and efficiency of rumor propagation control. First, to reduce the impact of rumor controlling mechanism on users’ normal activities, we introduce a soft dynamic quarantine strategy into rumor propagation control and develop a new propagation model named susceptible-infected-removed-quarantined ignorants-quarantined spreaders (SIRQU) to model and block the rumor propagation in the network. Second, to further improve the control efficiency, we propose an influential node selection algorithm based on discrete particle swarm optimization with an evolutionary search strategy, and the controlling mechanism is only applied on the most influential nodes. Finally, we conduct a series of simulations and experiments on several public datasets and the dataset collected from Sina Weibo to validate the proposed method, and the results show that the proposed method outperfoms the related baseline algorithms.

Recommender system, as a data-driven way to help customers locate products that match their interests, is increasingly critical for providing competitive customer suggestions in many web services. However, recommender systems are highly vulnerable to malicious injection attacks due to their fundamental vulnerabilities and openness. With the endless emergence of new attacks, how to provide a feasible way for defending different malicious threats against online recommendations is still an under-explored issue. In this paper, we explore a new way to defend malicious injection attacks through user intention understanding and influential neighbour disclosure. Specifically, we propose a detection approach, termedTBOS(ThreeBirds withOneStone), to deal with different malicious threats. InTBOS, we first develop the discrimination of attack target by combining global influence evaluation and risk attitude estimation of users. In order to makeTBOScontrollable, second, we propose to incorporate an optimal denoising mechanism to remove disturbed information before detection. To enhance the representativeness and predictability of detection model, finally, we propose to leverage a behavioral label propagation mechanism based on constructed label space for the determination of malicious injection behaviors. Extensive experiments on both synthetic and real data demonstrate thatTBOSoutperforms all baselines in different cases. Particularly, the detection performance ofTBOScan achieve an improvement of 6.08% FAR (false alarm rate) for optimal-injection attacks, an improvement of 3.83% FAR in average for co-visitation injection attacks, as well as an improvement of 2.3% for profile injection attacks over benchmarks in terms of FAR while keeping the highest DR (detection rate). Additional experiments on real-world data show thatTBOSbrings an improvement with the advantage of 6.5% FAR in average compared with baselines.

Device identification is of great importance in system management and network security. Especially, it is the priority in industrial internet of things (IIoT) scenario. Since there are massive devices producing various kinds of information in manufacturing process, the robustness, reliability, security and real-time control of the whole system is based on the identification of the massive IIoT devices. Previous IIoT device identification solutions are mostly based on a centralized architecture, which brings a lot of problems in scalability and security. In addition, most traditional identification systems can only identify inherent types of devices which is not suitable for the adaptive device management in IIoT. In order to solve these problems, this paper proposes a Intelligent Identification Scheme(IIS) of Massive IoT Devices, a completely distributed intelligent identification scheme of massive IIoT devices. The scheme changes the traditional centralized architecture and realizes more efficient clustering identification of massive IIoT devices. Moreover, IIS can identify more and more types of devices intelligently with the continuous learning ability since the identification model is constantly updated according to the ledger which is maintained by all gateways collaboratively. We also conduct experiments to evaluate the performance of IIS based on the data obtained from real IIoT devices, which proves that IIS is efficient in device identification and intelligent for the adaptive device management in IIoT.

With the flourish of the Web 2.0, online reviews offer valuable information for customers and businesses. Deep investigation on the online reviews can help the businesses understand customers and their needs, which can assist decision making in product design and marketing. However, the massive records with irregular structure and ambiguous words pose great challenges for online review analysis. In this paper, we focus on the movie reviews and propose a framework to mine the aspect-based opinions, and utilize the results for decision making support. Based on the different sentence characteristics of movie reviews collected from Douban, the most popular movie community in China, we divide the reviews into two categories, short reviews and long reviews. Firstly, we develop different methods to extract the fine-grained aspects including the global and local aspects from the short reviews and long reviews respectively. Secondly, a lexical updating algorithm is proposed to identify the opinion words towards different aspects. In contrast to most studies that focus on determining the overall sentiment orientation (positive versus negative), the proposed method performs fine-grained analysis to mine both the various aspects and their corresponding opinions of a movie. Finally, based on the positive and negative opinions towards different aspects, the producers can improve the marketing strategy and future products. Experimental results based on the data collected from Douban verify the efficiency and accuracy of the developed methods.

Anomaly detection is an important technique used to identify patterns of unusual network behavior and keep the network under control. Today, network attacks are increasing in terms of both their number and sophistication. To avoid causing significant traffic patterns and being detected by existing techniques, many new attacks tend to involve gradual adjustment of behaviors, which always generate incomplete sessions due to their running mechanisms. Accordingly, in this work, we employ the behavior symmetry degree to profile the anomalies and further identify unusual behaviors. We first proposed a symmetry degree to identify the incomplete sessions generated by unusual behaviors; we then employ a sketch to calculate the symmetry degree of internal hosts to improve the identification efficiency for online applications. To reduce the memory cost and probability of collision, we divide the IP addresses into four segments that can be used as keys of the hash functions in the sketch. Moreover, to further improve detection accuracy, a threshold selection method is proposed for dynamic traffic pattern analysis. The hash functions in the sketch are then designed using Chinese remainder theory, which can analytically trace the IP addresses associated with the anomalies. We tested the proposed techniques based on traffic data collected from the northwest center of CERNET (China Education and Research Network); the results show that the proposed methods can effectively detect anomalies in large-scale networks.

Users can participate in variety topics and express their opinions using different kinds of online applications, and the IDs (nickname) they used are usually virtual and difficult for finding the physical person. Which pose great challenges for network security management and user's online behavior supervision. Focus on this problem, we proposed methods for nickname detection based on the user's online connection structure and similarity propagation model. Firstly, we collected user's profile information from two popular online applications, sina microblog and RenRen network. Then we mark several matched pairs which belong to the same person from the applications with hardly manually effort, and those IDs are selected as seed set for nickname detection. Secondly, we proposed an nickname detection model based on the connection structure and similarity propagation. We selected one matched pairs from the seed set and obtain all their neighbors. Then we calculated the similarity of each pairs from the neighbor set and calculate their neighbors' connection similarity and neighbors' location similarity. If the similarity is bigger than a selected threshold, we claim they are matched pairs and insert them into the seed sets. On one hand, the correlation results can propagated based on the updated seed set. On the other hand, the computational complexity are greatly reduced as we only employ the neighbors' profiles to calculate the similarity. Experimental results verify the efficiency of the proposed method, which can lay a solid foundation for the online network management and user's behavior supervision.

With the number of Internet users and applications continues to grow, it becomes increasingly important to understand the users' behavior character for efficient network management and security monitoring. Web is one of the most popular applications, which can help users to obtain anything they want. In this paper, we propose a new framework to measure and monitor users' web access behavior character. Firstly, web services are divided into 12 types according to the content they provide. As user like to access different web services at different time instants, we proposed the behavior spectrum to describe the users' access character in an easily understandable way. Secondly, we employ three traffic features (number of connections, number of packets and number of bytes) to measure the intensity of users' access behavior. Based on the spectrums and features obtained, two kinds of user's access characters are analyzed: the characters at a particular time instant and the dynamic changing characters at continuous time points. Finally, we employ the Renyi entropy to cluster the users into different groups based on the spectrum and find the results are useful for network management. To verify the method, several actual traffic traces are collected from the Northwest Regional Center of CERNET (China Education and Research Network) and the experimental results prove the efficiency of the proposed methods.

In the field of computer-assisted cartography, cartographic generalization is one of the basic theories and methods of cartography. It is not only an abstract cognition of objective world, but also an important means of spatial information transformation and an important step of cartography. In digital environment, especially in GIS, all kinds of geographic entities and phenomena on the Earth's surface are abstracted as point, line, and polygon. Point cluster is one of the most important objects in the spatial analysis. In GIS, size, direction and shape of spatial points are not vital, however the holistic spatial configuration of spatial point cluster can represent the whole characteristic of point cluster. Thus, the whole spatial configuration of point cluster must be taken into account in cartographic generalization. In this paper, the former generalization methods of spatial point cluster are discussed in detail, including statistics, settlement-spacing ratio, gravity modeling, distribution-coefficient control, circle growth, convex hull and Delaunay triangulation structure. The advantages and disadvantages of those methods are appraised. For these methods, reduction of spatial points is settled commendably, but there are still insufficiencies of maintaining the boundary and spatial structure of spatial point cluster. In the end, the optimum method to improve on maintaining the structure of spatial point-cluster is set forth and the content of this method is designed in brief.