Network Entities Research Articles

Deep learning approach on information diffusion in heterogeneous networks

There are many real-world complex systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the well-known deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods.

Modeling a production-inventory-routing problem of blood products using heuristic solution methods

Blood and blood products are vital resources for the surgery and the treatment of certain diseases. As a scarce and perishable resource, they require sophisticated management to minimize waste in order to address this challenge, the present study revolves around the idea of the management of the pr oduction, supply and distribution of blood products. In this research, two questions of robust and flexible have been investigated for the production, inventory and routing blood products. The flexibility is incorporated into the problem through introducing the possibility of sharing inventory among network entities by transferring blood products between hospitals and also the possibility of meeting a blood group’s need with another compatible blood type or replacement. The problem is then solved by heuristic (local search) and meta-heuristic (Adaptive Large Neighborhood Search (ALNS)) algorithms, which are the methods of choice in particular for NP-hard problems. Finally, the results obtained from the two algorithms are compared it is shown that the heuristic algorithm outperforms the Adaptive Large Neighborhood Search (ALNS) in both models, that can lead to reduction is cost and required transitions.

THE MECHANISM OF INTRODUCTION OF CLUSTERS IN THE RUSSIAN FEDERATION

Purpose: The purpose of this article is to reveal distinctive features of clusters and determine whether those territorial economic entities that are being actively implemented in Russia today may be referred to as clusters.
 Methodology: Our analysis work made it possible to ascertain that clusters as networked (hybrid) entities have important distinctive features. The key to which we believe, along with the local base of formation, is to be considered the fact that being comprised of organizations of different types, they do not provide a single mechanism to coordinate interaction.
 Result: Study of the territorial entities originating in Russia shows that they cannot be referred to as clusters, mainly because of the administrative voluntarist mechanism of their formation.
 Applications: This research can be used for Students in mathematic and teachers.
 Novelty/Originality: In this research, the model of the mechanism of introduction of clusters in the Russian Federation is presented in a comprehensive and complete manner.

Network Transparency for Better Internet Security

The lack of transparency for Internet communication prevents effective mitigation of today's security threats: i) Source addresses cannot be trusted and enable untraceable reflection attacks. ii) Malicious communication is opaque to all network entities, except for the receiver; and although ISPs are control points that can stop such attacks, effective detection and mitigation requires information that is available only at the end hosts. We propose TRIS, an architecture that bootstraps transparency for Internet communication. TRIS enables the definition of misbehavior according to the unique requirements of hosts, and then it constructs verifiable evidence of misbehavior. First, hosts express desired traffic properties for incoming traffic; a deviation from these properties signifies misbehavior. Second, ISPs construct verifiable evidence of misbehavior for the traffic they forward. If misbehavior is detected, it can then be proven to the ISPs of the communicating hosts. We implement our architecture on commodity hardware and demonstrate that verifiable proof of misbehavior introduces little overhead with respect to bandwidth and packet processing in the network: our prototype achieves line-rate performance for common packet sizes, saturating a 10 Gbps link with a single CPU core. In addition, we tackle incremental deployment issues and describe interoperability with today's Internet architecture.

Network Topology of the States Probed by a Glassy Polymer during Physical Aging

AbstractTrajectories of a long‐chain atactic polystyrene system, obtained from long Molecular Dynamics simulations in the glassy state, are mapped onto a time series of inherent structures of the potential energy landscape. The topology of the network of states formed is analyzed by employing graph‐theoretical concepts. It is found that the network of the inherent structures visited by the system in the course of physical aging has both a scale‐free and a small‐world character. There exist minima acting as highly connected hubs in the network and trench‐like sequences of states driving the system from one hub to another. In contrast to previous studies of scale‐free networks in other branches of science, where the topology results from the static connectivity of the network's entities, this network is created by adding one‐by‐one the inherent structures traversed by the system during its evolution in time.

Extending association rules with graph patterns

We propose a general class of graph-pattern association rules (GPARs) for social network analysis. Extending association rules for itemsets, GPARs can help us discover associations among entities in social networks and identify potential customers. Despite the benefits, GPARs bring us challenges: the problem of GPARs discovery is already intractable, not to mention mining over large social networks. Nonetheless, we show that it is still feasible to discover GPARs from large social networks. We first formalize the GPARs mining problem and decompose it into two subproblems: Frequent pattern mining and rule generation. To address two subproblems, we develop a parallel algorithm along with an optimization strategy to construct DFS code graphs, whose nodes correspond to frequent patterns. We also provide efficient algorithms to generate (resp. representative) GPARs by using (resp. maximal) frequent patterns. Using real-life and synthetic graphs, we experimentally verify that our algorithms not only scale well but can also identify interesting GPARs with high quality among social entities.

Reliable p-Hub Network Design under Multiple Disruptions

The design of optimal hub-and-spoke networks has been the objective of many research studies. More recently, several studies in this area have been concerned with incorporating failures of network entities (e.g., hubs and/or links) as a source of uncertainty in the formulation and solution of reliable hub networks. This study is focused on modeling and developing a solution approach for the multi-allocation reliable p-hub network design problem where more than one hub may be disrupted simultaneously. The objective is to minimize the expected cost of the network under all possible failure scenarios. A mathematical formulation is presented to select hubs and determine primary and backup connections for each origin-destination (O-D) node pair. An algorithm is suggested to generate primary and backup connections for an O-D pair for a given set of hubs. The hub selection model is then solved using a search algorithm. The computational results show that near optimal solutions for non-trivial problem size instances are obtained in a reasonable amount of time.

Integrating Networking, Storage, and Computing for Resilient Battlefield Networks

The effectiveness of current military missions requires cooperation among different network entities of modern BN. Military missions have complex communication, storage, and computing requirements. Meanwhile, resilience is a very important capability for BN to survive disruptions and attacks. In this article, a new framework is proposed that integrates networking, storage, and computing resources to improve the resilience of BN. Three key components are defined in this framework: the intelligent data plane, the distributed management plane and the customized application plane. The intelligent data plane contains the facilities in charge of performing a set of manipulation strategies, including networking, storage, and computing operations. The distributed management plane consists of distributed controllers that are responsible for managing and scheduling resources of the data plane facilities. The customized application plane supports customized services for BN, including tactical data link, intelligence reconnaissance, and so on. The superiority of this new framework for BN is demonstrated by the simulation results. Besides, the open research issues of intelligence, safety, and effectiveness are also discussed.

A 3D dynamic visualization method coupled with an urban drainage model

The urban drainage model is an important tool for the management and decision-making around an urban drainage network. Owing to urban drainage networks’ complex distribution, existing model analysis methods have gradually exposed problems of insufficient utilization of spatial information, weak coupling and analytical capacity of multi-source heterogeneous data, and poor human-computer interaction. By coupling an urban drainage model with 3D GIS technology and 3D visualization technology, this study presents a 3D dynamic visualization method of an urban drainage model (3DDVM-UDM). A case study from Chaohu, China, is used to demonstrate the method. The environmental information and distributions of pipe network objects in 3D space were presented. The urban drainage model can be edited and simulated in the 3D environment, and a 2D flooding simulation method was also established. Multiple model results were dynamically and simultaneously visualized in the pipe network entity models. By coupling and comparing the model results under different rainfall return periods, various information was intuitively analyzed, such as the water level and water quality differences of each pipe and node, and the spatial distribution differences of fully filled pipes, overflowed nodes and buildings influenced by node overflow. The operation state of a drainage pipe network can be systematically analyzed by intuitively combining with the attribute information of a pipe network, 3D spatial information and other relevant data. This study provides a feasible theory and method to assist in environmental management and decision-making, to facilitate communication between different fields, and to promote a shift in analysis from 2D to the combination of 2D and 3D.

The assessment of food safety culture in small franchise restaurant in Poland

Purpose The purpose of this paper is to measure and assess the level of food safety culture (FSC) in the restaurant operating in the SUBWAY franchise network. Design/methodology/approach The case study accompanied by the questionnaire built of 38 items grouped into five areas/sections: management style/food safety (FS) policy, leadership, communication, commitment and work environment. Findings The level of FSC is 3.83 on a 1–5 scale. It requires further improvement and taking greater care of FS. The work environment and management style/FS policy were assessed the highest, while leadership and communication the lowest. There is a need to increase the frequency of meetings that address the issue of FS, ensure a better atmosphere of mutual trust and sharing knowledge about potential problems. It is also necessary to appreciate FS initiatives as well as review the number and suitability of existing procedures. Research limitations/implications In further studies, the case study accompanied by a questionnaire could be supported by additional qualitative methods, e.g. interviews with employees, observations or mystery shopping. Practical implications The results may help managers to verify and improve the overall FS policy in the restaurant and to encourage ongoing assessment of FSC in other franchise network entities that will take into account the critical aspects of FSC indicated in the case study. Originality/value In the literature, the issue of the role and meaning as well as the assessment of FSC has been undertaken for many years; however, there are no papers considering small restaurants from Central Europe. This is also the first paper in Poland that addresses the aspect of FSC and its assessment.

SAPSC: SignRecrypting authentication protocol using shareable clouds in VANET groups

Security and mutual reliability are the crucial requirements of an ad hoc network as nodes are dependent upon each other for routing and forwarding their messages. Vehicular ad hoc networks (VANETs) are no exception and are always at the risk of impersonation attack. An authentication protocol protects the identity of network entities from being impersonated. Occasionally, they require re-encryption technique which enables any other node to communicate on behalf of an unavailable node. The technology is used to provide backup in emergencies. We propose a secure authentication algorithm to efficiently re-encrypt the messages using signcryption. For faster computation and routing, the authors have used shareable clouds in VANET groups. Security of the protocol is proved using Burrows–Abadi–Needham logic and validated by simulation tool automated validation of internet security protocols and applications.

Deep Reinforcement Learning for Mobile 5G and Beyond: Fundamentals, Applications, and Challenges

Future-generation wireless networks (5G and beyond) must accommodate surging growth in mobile data traffic and support an increasingly high density of mobile users involving a variety of services and applications. Meanwhile, the networks become increasingly dense, heterogeneous, decentralized, and ad hoc in nature, and they encompass numerous and diverse network entities. Consequently, different objectives, such as high throughput and low latency, need to be achieved in terms of service, and resource allocation must be designed and optimized accordingly. However, considering the dynamics and uncertainty that inherently exist in wireless network environments, conventional approaches for service and resource management that require complete and perfect knowledge of the systems are inefficient or even inapplicable. Inspired by the success of machine learning in solving complicated control and decision-making problems, in this article we focus on deep reinforcement- learning (DRL)-based approaches that allow network entities to learn and build knowledge about the networks and thus make optimal decisions locally and independently. We first overview fundamental concepts of DRL and then review related works that use DRL to address various issues in 5G networks. Finally, we present an application of DRL for 5G network slicing optimization. The numerical results demonstrate that the proposed approach achieves superior performance compared with baseline solutions.

Identity Management and Access Control Based on Blockchain under Edge Computing for the Industrial Internet of Things

Edge computing provides a unified platform for computing, networking, and storage resources, enabling data to be processed in a timely and efficient manner near the source. Thus, it has become the basic platform for industrial Internet of things (IIoT). However, computing′s unique features have also introduced new security problems. To solve the problem, in this paper, blockchain-based identity management combining access control mechanism is designed under edge computing. The self-certified cryptography is utilized to realize the registration and authentication of network entities. We bind the generated implicit certificate to its identity and construct the identity and certificate management mechanism based on blockchain. Secondly, an access control mechanism based on Bloom filter is designed and integrated with identity management. Moreover, for secure communication in resource-constrained edge devices, a lightweight secret key agreement protocol based on self-authenticated public key is constructed. These mechanisms work together to provide data security guarantees for IIoT such as authentication, auditability, and confidentiality.

RFL: Robust fault localization on unreliable communication channels

The current Internet is vulnerable to various attacks, e.g., source spoofing and flow hijacking attacks, which are incurred by misconfigurations or attacks. Either users or network operators are unable to easily localize these faults. Existing fault localization mechanisms can detect such attacks under an assumption that localization is performed upon reliable communication channels. Unfortunately, the assumption does not always hold. The forwarding paths of localization are not always reliable. Packets are usually dropped for some reasons. In particular, adversaries can interfere with fault localization by maliciously dropping packets. In this paper, we relax the assumption and propose a robust data-plane fault localization protocol named RFL that can localize faults and achieve source authenticity and path compliance even if communication channels in the network are not reliable. RFL samples and verifies packets in each network entity so that the packet source can efficiently localize faults of packet forwarding by verifying the sampled packets. By leveraging packet acknowledgment, packet sampling based fault localization is not impacted by packet loss in the communication channels. In particular, RFL leverages a symmetric key distribution scheme to implement robust key distribution among different entities, which ensures that packet sources can always correctly fresh their keys to perform correct localization. Our security and theoretical analysis demonstrates the robustness of RFL protocol. We implement the RFL prototype on Click routers. The experiment results with the prototype demonstrate that RFL achieves more than 99.5% localization accuracy while incurring only 10% throughput degradation.

SDNMS: A software defined network measurement system for NFV networks

As a promising technology to completely transform how we architect, deploy, operate and manage various networks, software-based Network Function Virtualization (NFV) enables hardware-independent, flexible, fast and efficient network service provision. With the increasing popularity of NFV paradigm, the Internet has also transformed to be a hybrid environment where NFV-based network entities coexist with traditional network devices. To facilitate our understanding, design, evaluate and manage of such novel network environment, there is a great need for a new NFV-compatible network measurement system which is still in absent so far. To bridge this gap, a system, named Software Defined Network Measurement System (SDNMS), is presented in this paper. Firstly, the architecture of SDNMS is proposed. In this architecture, a formal method to describe the working mode of the network measurement is defined. This method can also be utilized to generate a network measurement middle box (NMMB) in a specific location of the NFV network according to the customized description file, and to flexibly deploy the network measurement function. Secondly, the technology of virtual network measurement function (VNMF) combined with LXC is studied to form NMMB function. Thirdly, a control method is presented to control the start, stop, and update NMMB to form a specific network measurement system function. Finally, a prototype of SDNMS with network monitoring function to monitor network performance anomalies and locate faults is introduced. Experimental results have shown that SDNMS architecture and related technologies are feasible and effective to deploy and control network measurement functions in NFV networks. We hope SDNMS could provide a new method for practitioners to conduct network management and research at the age of NFV.

End-To-End Mobility for the Internet Using ILNP

As the use of mobile devices and methods of wireless connectivity continue to increase, seamless mobility becomes more desirable and important. The current IETF Mobile IP standard relies on additional network entities for mobility management, can have poor performance, and has seen little deployment in real networks. We present a host-based mobility solution with a true end-to-end architecture using the Identifier-Locator Network Protocol (ILNP). We show how the TCP code in the Linux kernel can be extended allowing legacy TCP applications that use the standard C sockets API to operate over ILNP without requiring changes or recompilation. Our direct testbed performance comparison shows that ILNP provides better host mobility support than Mobile IPv6 in terms of session continuity, packet loss, and handoff delay for TCP.

An Attribute Credential Based Public Key Scheme for Fog Computing in Digital Manufacturing

In order to meet low latency, service sensitive and location awareness requirements of digital manufacturing, fog computing is introduced to be an intermediate layer between industrial Internet of Things and cloud. The distributed, dynamic characteristics and the collaboration requirement make it face many new security and privacy issues that cannot be solved by the traditional public key or symmetric cryptosystem. For addressing them, a registered but anonymous attribute credential is designed to manage the network entities. Based on it, an attribute credential based public key cryptography (AC-PKC) is constructed to provide flexible key management by taking the advantage of the certificate-less public key cryptography and the combination property of the elliptic curve cryptography. Encryption, authentication, and access control with privacy preserving can be realized on the basic operations of AC-PKC, which can meet various security requirements of fog computing based digital manufacturing. The performance analyses and comparison with the existing public key schemes and attribute based encryption solutions show that the proposed scheme can work flexibly at a relatively low cost.

Transforming India’s electoral democracy using Tantra framework

India has a rich tradition where good governance is highly valued. The theme of good governance is emphasized by a lineage of thought leaders from Kautilya in 230 BCE (परजासख सख राजञः परजाना च हित हितम । नातमपरिय हित राजञः परजाना त परिय हितम ॥ अरथशासतरः १.१९.४३ ॥ Chapter 19 titled राजपरणिधिः duties of the king in Arthashastra of Kautilya. The happiness of the king is in the happiness of the people, his welfare is in the welfare of the people. The welfare of the king lies not in what he desires, but what his subjects desire) to Mahatma Gandhi [Mahatma Gandhi propounded the concept of “Su-raj” and according to him “good governance” has the following eight attributes, which link it to its citizens: (1) accountable, (2) transparent, (3) responsive, (4) equitable and inclusive, (5) effective and efficient, (6) follows the rule of law, (7) participatory and (8) consensus oriented] in recent times. However, achieving good governance in a country of size of India is hard. Good governance needs to address social, cultural, ethical and process dimensions. Governance not only needs to be good and efficient, but needs to human-centric. At times even well-intentioned and well-thought out processes of Government cause lot of trauma to common citizens. On the contrary doing away with safeguards is inviting disaster. To meet the objective of human-centric good governance, we have developed Tantra social information management framework. Tantra framework makes use of concepts from Zachman framework and unified foundational ontology. The framework itself is modeled as a social network (entity–entity network). Tantra framework interoperates with models such as balanced score card, theory of change and Bartels’ theory of separations. This paper describes the Tantra framework and how it can be applied to transform India’s electoral democracy.

Network entity characterization and attack prediction

The devastating effects of cyber-attacks, highlight the need for novel attack detection and prevention techniques. Over the last years, considerable work has been done in the areas of attack detection as well as in collaborative defense. However, an analysis of the state of the art suggests that many challenges exist in prioritizing alert data and in studying the relation between a recently discovered attack and the probability of it occurring again. In this article, we propose a system that is intended for characterizing network entities and the likelihood that they will behave maliciously in the future. Our system, namely Network Entity Reputation Database System (NERDS), takes into account all the available information regarding a network entity (e.g. IP address) to calculate the probability that it will act maliciously. The latter part is achieved via the utilization of machine learning. Our experimental results show that it is indeed possible to precisely estimate the probability of future attacks from each entity using information about its previous malicious behavior and other characteristics. Ranking the entities by this probability has practical applications in alert prioritization, assembly of highly effective blacklists of a limited length and other use cases.

Mining Relationships among Multiple Entities in Biological Networks.

Identifying topological relationships among multiple entities in biological networks is critical towards the understanding of the organizational principles of network functionality. Theoretically, this problem can be solved using minimum Steiner tree (MSTT) algorithms. However, due to large network size, it remains to be computationally challenging, and the predictive value of multi-entity topological relationships is still unclear. We present a novel solution called Cluster-based Steiner Tree Miner (CST-Miner) to instantly identify multi-entity topological relationships in biological networks. Given a list of user-specific entities, CST-Miner decomposes a biological network into nested cluster-based subgraphs, on which multiple minimum Steiner trees are identified. By merging all of them into a minimum cost tree, the optimal topological relationships among all the user-specific entities are revealed. Experimental results showed that CST-Miner can finish in nearly log-linear time and the tree constructed by CST-Miner is close to the global minimum.