scholarly journals Fusing Node Embeddings and Incomplete Attributes by Complement-Based Concatenation

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zheng Wang ◽  
Yuexin Wu ◽  
Yang Bao ◽  
Jing Yu ◽  
Xiaohui Wang
Keyword(s):  
Critical Role ◽  
Inductive Method ◽  
Network Embedding ◽  
Parametric Function ◽  
Adjacency Graph ◽  
Network Nodes ◽  
Learning Tasks ◽  
Multiple Network ◽  
The Given ◽  
Node Embeddings

Network embedding that learns representations of network nodes plays a critical role in network analysis, since it enables many downstream learning tasks. Although various network embedding methods have been proposed, they are mainly designed for a single network scenario. This paper considers a “multiple network” scenario by studying the problem of fusing the node embeddings and incomplete attributes from two different networks. To address this problem, we propose to complement the incomplete attributes, so as to conduct data fusion via concatenation. Specifically, we first propose a simple inductive method, in which attributes are defined as a parametric function of the given node embedding vectors. We then propose its transductive variant by adaptively learning an adjacency graph to approximate the original network structure. Additionally, we also provide a light version of this transductive variant. Experimental results on four datasets demonstrate the superiority of our methods.

scholarly journals MultiVERSE: a multiplex and multiplex-heterogeneous network embedding approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Léo Pio-Lopez ◽  
Alberto Valdeolivas ◽  
Laurent Tichit ◽  
Élisabeth Remy ◽  
Anaïs Baudot
Keyword(s):  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Link Prediction ◽  
Network Embedding ◽  
Multiplex Networks ◽  
Multiplex Network ◽  
Gene Associations ◽  
Different Types ◽  
Embedding Methods ◽  
Node Embeddings

AbstractNetwork embedding approaches are gaining momentum to analyse a large variety of networks. Indeed, these approaches have demonstrated their effectiveness in tasks such as community detection, node classification, and link prediction. However, very few network embedding methods have been specifically designed to handle multiplex networks, i.e. networks composed of different layers sharing the same set of nodes but having different types of edges. Moreover, to our knowledge, existing approaches cannot embed multiple nodes from multiplex-heterogeneous networks, i.e. networks composed of several multiplex networks containing both different types of nodes and edges. In this study, we propose MultiVERSE, an extension of the VERSE framework using Random Walks with Restart on Multiplex (RWR-M) and Multiplex-Heterogeneous (RWR-MH) networks. MultiVERSE is a fast and scalable method to learn node embeddings from multiplex and multiplex-heterogeneous networks. We evaluate MultiVERSE on several biological and social networks and demonstrate its performance. MultiVERSE indeed outperforms most of the other methods in the tasks of link prediction and network reconstruction for multiplex network embedding, and is also efficient in link prediction for multiplex-heterogeneous network embedding. Finally, we apply MultiVERSE to study rare disease-gene associations using link prediction and clustering. MultiVERSE is freely available on github at https://github.com/Lpiol/MultiVERSE.

scholarly journals Full-Duplex NOMA Transmission with Single-Antenna Buffer-Aided Relays

Electronics ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 1482 ◽  
Author(s):  
Nikolaos Nomikos ◽  
Panagiotis Trakadas ◽  
Antonios Hatziefremidis ◽  
Voliotis
Keyword(s):  
Low Complexity ◽  
Cooperative Relaying ◽  
Full Duplex ◽  
Practical Implementation ◽  
Network Nodes ◽  
Communication Techniques ◽  
Single Antenna ◽  
Low Complexity Implementation ◽  
Significant Performance ◽  
Multiple Network

The efficient deployment of fifth generation and beyond networks relies upon the seamless combination of recently introduced transmission techniques. Furthermore, as multiple network nodes exist in dense wireless topologies, low-complexity implementation should be promoted. In this work, several wireless communication techniques are considered for improving the sum-rate performance of cooperative relaying non-orthogonal multiple access (NOMA) networks. For this purpose, an opportunistic relay selection algorithm is developed, employing single-antenna relays to achieve full-duplex operation by adopting the successive relaying technique. In addition, as relays are equipped with buffers, flexible half-duplex transmission can be performed when packets reside in the buffers. The proposed buffer-aided and successive single-antenna (BASSA-NOMA) algorithm is presented in detail and its operation and practical implementation aspects are thoroughly analyzed. Comparisons with other relevant algorithms illustrate significant performance gains when BASSA-NOMA is employed without incurring high implementation complexity.

scholarly journals An Unorthodox Parametric Measure of Information and Corresponding Measure of Intutionistic Fuzzy Information

2013 ◽  
Vol 3 ◽  
pp. 24-27
Author(s):  
P. Jha ◽  
Vikas Kumar Mishra
Keyword(s):  
Probability Distribution ◽  
Fuzzy Entropy ◽  
Entropy Measure ◽  
Divergence Measure ◽  
Fuzzy Information ◽  
Parametric Function ◽  
Directed Divergence ◽  
The Given ◽  
Monotonic Character ◽  
Related Measure

A new parametric function va(p)= log (1+a) - Σlog((1+api)/pi)-1,a>o is proposed for the probability distribution p1p2p3… … … pn and its properties are studied. In this paper the given functions is twice differentiable and is used to obtain the related measure of directed divergence, measure of intutionistic fuzzy entropy, measure of intutionistic fuzzy directed divergence. We also investigate the monotonic character of the proposed function.

scholarly journals Multipath Redundant Real-Time Transmission with the Destruction of Expired Packets in Intermediate Nodes

2021 ◽  
Author(s):  
Vladimir Bogatyrev ◽  
Stanislav Bogatyrev ◽  
Anatoly Bogatyrev
Keyword(s):  
Real Time ◽  
Waiting Time ◽  
Data Transmission ◽  
Single Channel ◽  
Packet Transmission ◽  
Total Delay ◽  
Network Nodes ◽  
Time Operation ◽  
Real Time Operation ◽  
The Given

The possibilities of increasing the probability of timely service and reducing the average waiting time for requests for machine-to-machine exchange in distributed computer systems are investigated. Improving the reliability, timeliness and error-free transmission in automated distributed control systems focused on intelligent and cognitive methods of data and image analysis is fundamental in their real-time operation. The effect is achieved as a result of the reserved multipath transfers of packets critical to delays, at which their replication is provided with a task for each replica of the path (route) of sequential passage of network nodes. An analytical model is proposed for estimating the probability of timely delivery and the average total waiting time in the queues of route nodes with reserved and non-reserved packet transmission. The communication nodes that make up the data transmission route are represented by single-channel queuing systems with an infinite queue. The influence of the multiplicity of redundancy (replication) of transmissions on the probability of their timely maintenance is analyzed. The condition for the success of reserved transfers is that the accumulated total waiting in the queues of nodes that make up the path for at least one of the replicas of the packet should not exceed the given maximum allowed time. The efficiency of destroying expired packets in the intermediate nodes that make up the data transmission paths is shown. The existence of an optimal redundancy multiplicity critical to the total delay in the queues of packets is shown.

scholarly journals Layer Information Similarity Concerned Network Embedding

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Ruili Lu ◽  
Pengfei Jiao ◽  
Yinghui Wang ◽  
Huaming Wu ◽  
Xue Chen
Keyword(s):  
Structural Information ◽  
Single Layer ◽  
Network Embedding ◽  
Network Layers ◽  
Multiplex Network ◽  
Proposed Model ◽  
Latent Space ◽  
Comparable Performance ◽  
The Common ◽  
Node Embeddings

Great achievements have been made in network embedding based on single-layer networks. However, there are a variety of scenarios and systems that can be presented as multiplex networks, which can reveal more interesting patterns hidden in the data compared to single-layer networks. In the field of network embedding, in order to project the multiplex network into the latent space, it is necessary to consider richer structural information among network layers. However, current methods for multiplex network embedding mostly focus on the similarity of nodes in each layer of the network, while ignoring the similarity between different layers. In this paper, for multiplex network embedding, we propose a Layer Information Similarity Concerned Network Embedding (LISCNE) model considering the similarities between layers. Firstly, we introduce the common vector for each node shared by all layers and layer vectors for each layer where common vectors obtain the overall structure of the multiplex network and layer vectors learn semantics for each layer. We get the node embeddings in each layer by concatenating the common vectors and layer vectors with the consideration that the node embedding is related not only to the surrounding neighbors but also to the overall semantics. Furthermore, we define an index to formalize the similarity between different layers and the cross-network association. Constrained by layer similarity, the layer vectors with greater similarity are closer to each other and the aligned node embedding in these layers is also closer. To evaluate our proposed model, we conduct node classification and link prediction tasks to verify the effectiveness of our model, and the results show that LISCNE can achieve better or comparable performance compared to existing baseline methods.

Ensemble Learning for Regression

2011 ◽  
pp. 777-782
Author(s):  
Niall Rooney
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Neural Networks ◽  
Ensemble Learning ◽  
Learning Task ◽  
General Definition ◽  
Learning Tasks ◽  
Continuous Output ◽  
Input Variables ◽  
The Given

The concept of ensemble learning has its origins in research from the late 1980s/early 1990s into combining a number of artificial neural networks (ANNs) models for regression tasks. Ensemble learning is now a widely deployed and researched topic within the area of machine learning and data mining. Ensemble learning, as a general definition, refers to the concept of being able to apply more than one learning model to a particular machine learning problem using some method of integration. The desired goal of course is that the ensemble as a unit will outperform any of its individual members for the given learning task. Ensemble learning has been extended to cover other learning tasks such as classification (refer to Kuncheva, 2004 for a detailed overview of this area), online learning (Fern & Givan, 2003) and clustering (Strehl & Ghosh, 2003). The focus of this article is to review ensemble learning with respect to regression, where by regression, we refer to the supervised learning task of creating a model that relates a continuous output variable to a vector of input variables.

Service Providers Indexing Using P2P Systems

2010 ◽  
pp. 852-889
Author(s):  
G. Marchetto ◽  
M. Papa Manzillo ◽  
L. Torrero ◽  
L. Ciminiera ◽  
F. Risso
Keyword(s):  
Service Providers ◽  
Service Selection ◽  
Peer To Peer ◽  
Shared Resources ◽  
Network Nodes ◽  
P2p Systems ◽  
Natural Solution ◽  
Multiple Network ◽  
Cost Parameters ◽  
Topological Proximity

The idea of sharing resources across the network has become very popular during the last few years, leading to a diversified scenario in which shared resources include not only files and videos but also storage and CPU cycles. A new trend is to extend this paradigm toward a distributed architecture in which multiple network nodes cooperate to provide services in a distributed fashion, thus ensuring robustness and scalability. Peer-to-peer (P2P) overlays are the natural solution to achieve this goal as, thanks to their simplicity and flexibility, they can change their topology in order to fit the needs of the different kinds of services that can be provided on top of them. This chapter focuses on the indexing of nodes (i.e., the service providers) in these P2P systems, presenting the state-of-the-art solutions concerning the P2P-based indexing architectures and a taxonomy of possible services that can be built upon different overlay structures. Finally, emphasis is given to mechanisms implementing service selection based on some cost parameters (e.g., topological proximity) in order to introduce some mechanism based on optimality in case multiple service providers exist.

scholarly journals Modeling Neural Activity

2013 ◽  
Vol 2013 ◽  
pp. 1-37 ◽  
Author(s):  
Wim van Drongelen
Keyword(s):  
Critical Role ◽  
Network Models ◽  
Network Activity ◽  
Neural Function ◽  
Population Activity ◽  
Network Nodes ◽  
Neural Excitability ◽  
Network Function ◽  
Incomplete Datasets ◽  
Insight Into

This paper provides an overview of different types of models for studying activity of nerve cells and their networks with a special emphasis on neural oscillations. One part describes the neuronal models based on the Hodgkin and Huxley formalism first described in the 1950s. It is discussed how further simplifications of this formalism enable mathematical analysis of the process of neural excitability. The focus of the paper’s second component is on network activity. Understanding network function is one of the important frontiers remaining in neuroscience. At present, experimental techniques can only provide global recordings or samples of the activity of the huge networks that form the nervous system. Models in neuroscience can therefore play a critical role by providing a framework for integration of necessarily incomplete datasets, thereby providing insight into the mechanisms of neural function. Network models can either explicitly contain individual network nodes that model the neurons, or they can be based on representations of compound population activity. The latter approach was pioneered by Wilson and Cowan in the 1970s. Finally I provide an overview and discuss how network models are employed in the study of neuronal network pathology such as epilepsy.

Embedded Dynamic Security Networking Technology Based on Fast Jump and Trust Degree

2014 ◽  
Vol 602-605 ◽  
pp. 2773-2775
Author(s):  
Wen Kui Zheng ◽  
Man Jin
Keyword(s):  
Embedded System ◽  
Security Risks ◽  
Network Systems ◽  
Network Nodes ◽  
Dynamic Security ◽  
Network Search ◽  
Trust Degree ◽  
Multiple Network ◽  
Security Of Network ◽  
Networking Technology

The traditional embedded system networking technology ignores the security of network systems, either ignores fast networking requirements of network system, these requirements can be achieved at the same time. A embedded dynamic security networking technology based on fast jump and trust degree is proposed, when systems is networking, the use of random beating from multiple network nodes and fast switching optimize network search methods. Experimental results show that the proposed method can detect distribution of systems security risks accurately, and has a good application value for network security.

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