BSIN: A Behavior Schema of Information Networks Based on Approximate Bisimulation

Information networks are becoming increasingly important in practice. However, their escalating complexity is gradually impeding the efficiency of data mining. A novel network schema called the Behavior Schema of Information Networks (BSIN) is proposed to address this issue. This work defines the behavior of nodes as connected paths in BSIN, proposes a novel function distinguish behavior differences, and introduces approximate bisimulation into the acquisition of quotient sets for node types. The major highlight of BSIN is its ability to directly obtain a high-efficiency network on the basis of approximate bisimulation, rather than reducing the existing information network. It provides an effective representation of information networks, and the resulting novel network has a simple structure that more efficiently expresses semantic information than current network representations. The theoretical analysis of the connected paths between the original and the obtained networks demonstrates that errors are controllable;and semantic information is approximately retained. Case studies show that BSIN yields a simple network and is highly cost-effective.

Source-Aware Embedding Training on Heterogeneous Information Networks

Heterogeneous information networks(HINs)have been extensively applied to real-world tasks,such as recommendation systems,social networks,and citation networks.While existing HIN representation learning methods can effectively learn the semantic and structural features in the network,little awareness was given to the distribution discrepancy of subgraphs within a single HIN.However,we find that ignoring such distribution discrepancy among subgraphs from multiple sources would hinder the effectiveness of graph embedding learning algorithms.This motivates us to propose SUMSHINE(Scalable Unsupervised Multi-Source Heterogeneous Information Network Embedding)-a scalable unsupervised framework to align the embedding distributions among multiple sources of an HiN.Experimental results on real-world datasets in a variety of downstream tasks validate the performance of our method over the state-of-the-art heterogeneous information network embedding algorithms.

Information Freshness-Oriented Trajectory Planning and Resource Allocation for UAV-Assisted Vehicular Networks

In this paper,multi-UAV trajectory planning and resource allocation are jointly investigated to improve the information freshness for vehicular networks,where the vehicles collect time-critical traffic information by on-board sensors and upload to the UAVs through their allocated spectrum resource.We adopt the expected sum age of information(ESAoI)to measure the network-wide information freshness.ESAoI is jointly affected by both the UAVs trajectory and the resource allocation,which are coupled with each other and make the analysis of ESAoI challenging.To tackle this challenge,we introduce a joint trajectory planning and resource allocation procedure,where the UAVs firstly fly to their destinations and then hover to allocate resource blocks(RBs)during a time-slot.Based on this procedure,we formulate a trajectory planning and resource allocation problem for ESAoI minimization.To solve the mixed integer nonlinear programming(MINLP)problem with hybrid decision variables,we propose a TD3 trajectory planning and Round-robin resource allocation(TTPRRA).Specifically,we exploit the exploration and learning ability of the twin delayed deep deterministic policy gradient algorithm(TD3)for UAVs trajectory planning,and utilize Round Robin rule for the optimal resource allocation.With TTP-RRA,the UAVs obtain their flight velocities by sensing the locations and the age of information(AoI)of the vehicles,then allocate the RBs to the vehicles in a descending order of AoI until the remaining RBs are not sufficient to support another successful uploading.Simulation results demonstrate that TTP-RRA outperforms the baseline approaches in terms of ESAoI and average AoI(AAoI).

Research on Heterogeneous Information Network Link Prediction Based on Representation Learning

A heterogeneous information network,which is composed of various types of nodes and edges,has a complex structure and rich information content,and is widely used in social networks,academic networks,e-commerce,and other fields.Link prediction,as a key task to reveal the unobserved relationships in the network,is of great significance in heterogeneous information networks.This paper reviews the application of presentation-based learning methods in link prediction of heterogeneous information networks.This paper introduces the basic concepts of heterogeneous information networks,and the theoretical basis of representation learning,and discusses the specific application of the deep learning model in node embedding learning and link prediction in detail.The effectiveness and superiority of these methods on multiple real data sets are demonstrated by experimental verification.

Research on College Network Information Security Protection in the Digital Economy Era

In the era of the digital economy,the informatization degree of various industries is getting deeper and deeper,and network information security has also come into people’s eyes.Colleges and universities are in the position of training applied talents,because of the needs of teaching and education,as well as the requirements of teaching reform,the information construction of colleges and universities has been gradually improved,but the problem of network information security is also worth causing people to ponder.The low security of the network environment will cause college network information security leaks,and even hackers will attack the official website of the university and leak the personal information of teachers and students.To solve such problems,this paper studies the protection of college network information security against the background of the digital economy era.This paper first analyzes the significance of network information security protection,then points out the current and moral problems,and finally puts forward specific countermeasures,hoping to create a safe learning environment for teachers and students for reference.

Research of multi-path routing based on network coding in space information networks

A multi-path routing algorithm based on network coding is proposed for combating long propagation delay and high bit error rate of space information networks. On the basis of traditional multi-path routing, the algorithm uses a random linear network coding strategy to code data pack- ets. Code number is determined by the next hop link status and the number of current received packets sent by the upstream node together. The algorithm improves retransmission and cache mechanisms through using redundancy caused by network coding. Meanwhile, the algorithm also adopts the flow distribution strategy based on time delay to balance network load. Simulation results show that the proposed routing algorithm can effectively improve packet delivery rate, reduce packet delay, and enhance network performance.

Architecture and critical technologies of space information networks

Focusing on its main requirements and challenges and by analyzing the characteristics of different space platforms,an overall architecture for space information networks is proposed based on national strategic planning and the present development status of associated technologies.Furthermore,the core scientific problems that need to be solved are expounded.In addition,the primary considerations and a preliminary integrated demonstration environment for verification of key technologies are presented.

Concurrent Reconfiguration of Resource-Oriented Emergency TT&C Mission Planning for Space Information Networks

The scale expansion of the space information networks(SINs)makes the demands for tacking,telemetry and command(TT&C)missions increase dramatically.An increasing number of missions and a sharp conflict of resources make it much more challenging to schedule missions reasonably.In order to ensure both the mission completion rate of the high concurrent emergency missions and the performance of regular missions,a conflict degree scheduling algorithm based on transfer strategy(CDSA-TS)is proposed concurrently reconfiguring multi-dimensional resources reasonably.Furthermore,we design an emergency mission planning algorithm based on simulated annealing algorithm(EMPA-SA)to increase the probability of jumping out of the trap through the iterative neighborhood searching strategy and destabilization.Finally,we design a simulation system to verify the network performance in terms of the integrated weights of completed missions and the time consumption of the proposed algorithms.We also investigate the impact of the scheduling strategy for emergency missions on regular missions to improve the overall network performance,which provides guidance for emergency mission planning in the future for the large scale constellation oriented SINs.

Utilization and Analysis of Resource Mobility in Space Information Networks

In space information networks,resource mobility is an important factor affecting the network performance,which not only results in challenges in resource management but also brings opportunities to the improvement of network service capability.In order to explore the restriction and improvement mechanism of resource mobility on network performance,we firstly use the time-expanded resource relationship graph to represent the moving behavior of multidimensional resources and the collaborative relationship between different resources.Then by jointly considering the number of disjoint resource combinations,the length of moving time window,and the parameter of resource independence,we propose(k,n,L)degree of freedom on resource combination as a metric measuring performance gain resulted from resource mobility.Furthermore,the analysis of resource mobility is transformed into the problem of finding disjoint paths in the graph,and the tradeoff relationship between QoS requirements of task and resource mobility utilization is discussed.Finally,the tradeoff between the gain of resource mobility utilization and the payment of service process delay is revealed through simulation.

Information networks fusion based on multi-task coordination

Information networks provide a powerful representation of entities and the relationships between them.Information networks fusion is a technique for information fusion that jointly reasons about entities,links and relations in the presence of various sources.However,existing methods for information networks fusion tend to rely on a single task which might not get enough evidence for reasoning.In order to solve this issue,in this paper,we present a novel model called MC-INFM(information networks fusion model based on multi-task coordination).Different from traditional models,MC-INFM casts the fusion problem as a probabilistic inference problem,and collectively performs multiple tasks(including entity resolution,link prediction and relation matching)to infer the final result of fusion.First,we define the intra-features and the inter-features respectively and model them as factor graphs,which can provide abundant evidence to infer.Then,we use conditional random field(CRF)to learn the weight of each feature and infer the results of these tasks simultaneously by performing the maximum probabilistic inference.Experiments demonstrate the effectiveness of our proposed model.

An Anti-Quantum Authentication Protocol for Space Information Networks Based on Ring Learning with Errors

With the continuous development of satellite communication and Internet of things technology,more and more devices can access space information networks(SIN)and enjoy satellite services everywhere in the world.However,due to the openness of the air-to-ground channel,the device will face a series of security threats when accessing SIN,such as replay attacks,eavesdropping attacks,impersonation attacks,and man-in-the-middle attacks.These security threats will lead to illegal entity access and further endanger the reliability and availability of the system.Although scholars have proposed many enhanced security access authentication protocols,most of them have been proved to have security vulnerabilities.In addition,with the development of quantum computing,the previous authentication protocols based on some asymmetric cryptographic mechanisms such as discrete logarithm and elliptic curve cryptographic mechanisms may face new security challenges.In this context,this paper proposes a novel antiquantum access authentication protocol based on ring learning with errors(RLWE),which meets more security requirements and greatly reduces the authentication delay through prenegotiation.Through the security analysis and performance analysis,it is shown that our protocol can be more suitable for delaysensitive IoT devices to access SIN while ensuring higher security.

Meta-Path-Based Search and Mining in Heterogeneous Information Networks

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user＇s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.

Survey on key security technologies for space information networks

SIN(Space Information Network)is expected to play an increasing role in providing real-time,flexible,and integrated communication and data transmission services in an efficient manner.Nowadays,SIN has been widely developed for position navigation,environment monitor,traffic management,counter-terrorism,etc.However,security is a major concern in SIN,since the satellites,spacecrafts,and aircrafts are susceptible to a variety of traditional/specific network-based attacks,including eavesdropping,session hijacking,and illegal accessing.The network architecture and security issues of SIN were reviewed.Various security requirements were discussed that should be considered when designing SIN.And existing solutions proposed to meet these requirements were surveyed.The key challenges and key technologies that still require extensive research and development for securing SIN were indentifed.

Relaying Strategy Based on Estimated Information for Multi-Antenna Cooperative Networks

A sphere-based list forwarding scheme for multiple-input multiple-output(MIMO) relay networks is proposed and analyzed. Firstly, an estimate forwarding(EF) method is proposed, which forwards the minimum mean squared error(MMSE) estimate of the source data to the destination. Since it performs like amplify-and-forward(AF) and decode-and-forward(DF) for the low and high signal-to-noise ratio(SNR) regions, respectively, the EF relay thus outperforms conventional AF and DF across all SNRs without the need for switching algorithms for different SNRs. Because computational complexity is however high for relays with a large number of antennas(large MIMO) and/or high order constellations, list EF for large MIMO relay networks is proposed. It computes a list sphere decoder based MMSE estimate and retains the advantages of the exact EF relay at a negligible performance loss. The proposed list EF could offer a flexible trade-off between the performance and computational complexity.

Smart control plane for information centric network-internet service provider networks

The information centric network(ICN)has been widely discussed in current researches.The ICN interoperation with a traditional IP network and caching methods are one of the research topics of interest.For economic reasons,the capability of applying the ICN to internet service providers(ISPs)with various traditional IP protocols already implemented,especially IGP,MPLS,VRF,and TE,does not require any change on the IP network infrastructure.The biggest concern of ISPs is related to their customers’contents delivery speed.In this paper,we consider ICN caching locations in ISP by using the concept of locator/ID separation protocol(LISP)for interoperation between a traditional IP address and name-based ICN.To be more specific,we propose a new procedure to determine caching locations in the ICN by using the cuckoo search algorithm(CSA)for finding the best caching locations of information chunks.Moreover,we create the smart control plane(SCP)scheme which is an intelligent controlling,managing,and mapping system.Its function is similar to the software defined network concept.We show how the proposed SCP system works in both synthetic small network and real-world big network.Finally,we show and evaluate the performance of our algorithm comparison with the simple search method using the shortest path first algorithm.

Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks

In this paper,we investigate the evolution of spatiotemporal patterns and synchronization transitions in dependence on the information transmission delay and ion channel blocking in scale-free neuronal networks.As the underlying model of neuronal dynamics,we use the Hodgkin-Huxley equations incorporating channel blocking and intrinsic noise.It is shown that delays play a significant yet subtle role in shaping the dynamics of neuronal networks.In particular,regions of irregular and regular propagating excitatory fronts related to the synchronization transitions appear intermittently as the delay increases.Moreover,the fraction of working sodium and potassium ion channels can also have a significant impact on the spatiotemporal dynamics of neuronal networks.As the fraction of blocked sodium channels increases,the frequency of excitatory events decreases,which in turn manifests as an increase in the neuronal synchrony that,however,is dysfunctional due to the virtual absence of large-amplitude excitations.Expectedly,we also show that larger coupling strengths improve synchronization irrespective of the information transmission delay and channel blocking.The presented results are also robust against the variation of the network size,thus providing insights that could facilitate understanding of the joint impact of ion channel blocking and information transmission delay on the spatiotemporal dynamics of neuronal networks.

Resource Mobility Aware Hybrid Task Planning in Space Information Networks

In this paper,a resource mobility aware two-stage hybrid task planning algorithm is proposed to reduce the resource conflict between emergency tasks and the common tasks,so as to improve the overall performance of space information networks.Specifically,in the common task planning stage,a resource fragment avoidance task planning algorithm is proposed,which reduces the contention between emergency tasks and the planned common tasks in the next stage by avoiding the generation of resource fragments.For emergency tasks,we design a metric to quantify the revenue of the candidate resource combination of emergency tasks,which considers both the priority of the tasks and the impact on the planned common tasks.Based on this,a resource mobility aware emergency task planning algorithm is proposed,which strikes a good balance between improving the sum priority and avoiding disturbing the planned common tasks.Finally,simulation results show that the proposed algorithm is superior to the existing algorithms in both the sum task priority and the task completion rate.

Research on Network Cognition Model and Mechanism of Intelligent Information Network

Intellectualization has been an inevitable trend in the information network,allowing the network to achieve the capabilities of self-learning,self-optimization,and self-evolution in the dynamic environment.Due to the strong adaptability to the environment,the cognitive theory methods from psychology gradually become an excellent approach to construct the intelligent information network(IIN),making the traditional definition of the intelligent information network no longer appropriate.Moreover,the thinking capability of existing IINs is always limited.This paper redefines the intelligent information network and illustrates the required properties of the architecture,core theory,and critical technologies by analyzing the existing intelligent information network.Besides,we innovatively propose a novel network cognition model with the network knowledge to implement the intelligent information network.The proposed model can perceive the overall environment data of the network and extract the knowledge from the data.As the model’s core,the knowledge guides the model to generate the optimal decisions adapting to the environmental changes.At last,we present the critical technologies needed to accomplish the proposed network cognition model.

ESR-PINNs:Physics-informed neural networks with expansion-shrinkage resampling selection strategies

Neural network methods have been widely used in many fields of scientific research with the rapid increase of computing power.The physics-informed neural networks(PINNs)have received much attention as a major breakthrough in solving partial differential equations using neural networks.In this paper,a resampling technique based on the expansion-shrinkage point(ESP)selection strategy is developed to dynamically modify the distribution of training points in accordance with the performance of the neural networks.In this new approach both training sites with slight changes in residual values and training points with large residuals are taken into account.In order to make the distribution of training points more uniform,the concept of continuity is further introduced and incorporated.This method successfully addresses the issue that the neural network becomes ill or even crashes due to the extensive alteration of training point distribution.The effectiveness of the improved physics-informed neural networks with expansion-shrinkage resampling is demonstrated through a series of numerical experiments.

Cooperative Caching Strategy Based on Two-Layer Caching Model for Remote Sensing Satellite Networks

In Information Centric Networking(ICN)where content is the object of exchange,in-network caching is a unique functional feature with the ability to handle data storage and distribution in remote sensing satellite networks.Setting up cache space at any node enables users to access data nearby,thus relieving the processing pressure on the servers.However,the existing caching strategies still suffer from the lack of global planning of cache contents and low utilization of cache resources due to the lack of fine-grained division of cache contents.To address the issues mentioned,a cooperative caching strategy(CSTL)for remote sensing satellite networks based on a two-layer caching model is proposed.The two-layer caching model is constructed by setting up separate cache spaces in the satellite network and the ground station.Probabilistic caching of popular contents in the region at the ground station to reduce the access delay of users.A content classification method based on hierarchical division is proposed in the satellite network,and differential probabilistic caching is employed for different levels of content.The cached content is also dynamically adjusted by analyzing the subsequent changes in the popularity of the cached content.In the two-layer caching model,ground stations and satellite networks collaboratively cache to achieve global planning of cache contents,rationalize the utilization of cache resources,and reduce the propagation delay of remote sensing data.Simulation results show that the CSTL strategy not only has a high cache hit ratio compared with other caching strategies but also effectively reduces user request delay and server load,which satisfies the timeliness requirement of remote sensing data transmission.