scholarly journals Motif-h: a novel functional backbone extraction for directed networks

2021 ◽  
Author(s):  
Yiguang Bai ◽  
Qian Li ◽  
Yanni Fan ◽  
Sanyang Liu
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Weak Ties ◽  
The Novel ◽  
Directed Networks ◽  
Communication Architecture ◽  
Dense Networks ◽  
Edge Strength ◽  
Social Analytics ◽  
Large Scale Networks

AbstractDense networks are very pervasive in social analytics, biometrics, communication, architecture, etc. Analyzing and visualizing such large-scale networks are significant challenges, which are generally met by reducing the redundancy on the level of nodes or edges. Motifs, patterns of the higher order organization compared with nodes and edges, are recently found to be the novel fundamental unit structures of complex networks. In this work, we proposed a novel motif h-backbone (Motif-h) method to extract functional cores of directed networks based on both motif strength and h-bridge. Compared with the state-of-the-art method Motif-DF and Entropy, our method solves two main issues which are often found in existing methods: the Motif-h reconsiders weak ties into our candidate set, and those weak ties often have critical functions of bridges in networks; moreover, our method provides a trade-off between the motif size and the edge strength, which quantifies the core edges accordingly. In the simulations, we compare our method with Motif-DF in four real-world networks and found that Motif-h can streamline the extraction of crucial structures compared with the others with limited edges.

A Scalable Redefined Stochastic Blockmodel

2021 ◽  
Vol 15 (3) ◽  
pp. 1-28
Author(s):  
Xueyan Liu ◽  
Bo Yang ◽  
Hechang Chen ◽  
Katarzyna Musial ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Large Scale ◽  
Network Science ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Real World Data ◽  
Computational Overhead ◽  
Stochastic Blockmodel ◽  
Np Hard Problem ◽  
Large Scale Networks ◽  
The Cost

Stochastic blockmodel (SBM) is a widely used statistical network representation model, with good interpretability, expressiveness, generalization, and flexibility, which has become prevalent and important in the field of network science over the last years. However, learning an optimal SBM for a given network is an NP-hard problem. This results in significant limitations when it comes to applications of SBMs in large-scale networks, because of the significant computational overhead of existing SBM models, as well as their learning methods. Reducing the cost of SBM learning and making it scalable for handling large-scale networks, while maintaining the good theoretical properties of SBM, remains an unresolved problem. In this work, we address this challenging task from a novel perspective of model redefinition. We propose a novel redefined SBM with Poisson distribution and its block-wise learning algorithm that can efficiently analyse large-scale networks. Extensive validation conducted on both artificial and real-world data shows that our proposed method significantly outperforms the state-of-the-art methods in terms of a reasonable trade-off between accuracy and scalability. 1

Design of Structural Health Monitoring System Based on WSNs

2014 ◽  
Vol 681 ◽  
pp. 47-50
Author(s):  
Yue Zhou ◽  
Shuai Liu ◽  
Li Xin Zhang
Keyword(s):  
Structural Health Monitoring ◽  
Monitoring System ◽  
Health Monitoring ◽  
Large Scale ◽  
State Of The Art ◽  
Health Monitoring System ◽  
Structural Health ◽  
Structural Health Monitoring System ◽  
Large Scale Networks ◽  
Monitoring Application

The structural health monitoring technology has been one of the most important issues. In this paper, the design of wireless sensor network for structural health monitoring application is studied. The basic concept, significance, state of the art of structural health monitoring, the architecture and the principle of the wireless structural health monitoring system are described. The hardware and software of the overall system are designed and built. The WLANonSAN architecture network is particularly proposed as a solution for the large-scale networks.

scholarly journals SepNE: Bringing Separability to Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4261-4268 ◽  
Author(s):  
Ziyao Li ◽  
Liang Zhang ◽  
Guojie Song
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Dynamic Networks ◽  
Distributed Learning ◽  
Network Embedding ◽  
Large Networks ◽  
Comparable Accuracy ◽  
Large Scale Networks ◽  
Low Dimensional ◽  
Almost All

Many successful methods have been proposed for learning low dimensional representations on large-scale networks, while almost all existing methods are designed in inseparable processes, learning embeddings for entire networks even when only a small proportion of nodes are of interest. This leads to great inconvenience, especially on super-large or dynamic networks, where these methods become almost impossible to implement. In this paper, we formalize the problem of separated matrix factorization, based on which we elaborate a novel objective function that preserves both local and global information. We further propose SepNE, a simple and flexible network embedding algorithm which independently learns representations for different subsets of nodes in separated processes. By implementing separability, our algorithm reduces the redundant efforts to embed irrelevant nodes, yielding scalability to super-large networks, automatic implementation in distributed learning and further adaptations. We demonstrate the effectiveness of this approach on several real-world networks with different scales and subjects. With comparable accuracy, our approach significantly outperforms state-of-the-art baselines in running times on large networks.

Predicting missing links in directed networks based on local network structure and investment theory

2020 ◽  
Vol 31 (07) ◽  
pp. 2050096
Author(s):  
Jinsong Li ◽  
Jianhua Peng ◽  
Shuxin Liu ◽  
Xinsheng Ji
Keyword(s):  
Link Prediction ◽  
Statistical Physics ◽  
Large Scale ◽  
Empirical Studies ◽  
Local Network ◽  
Directed Networks ◽  
Local Index ◽  
Investment Theory ◽  
Large Scale Networks

As an elementary task in statistical physics and network science, link prediction has attracted great attention of researchers from many fields. While numerous similarity-based indices have been designed for undirected networks, link prediction in directed networks has not been thoroughly studied yet. Among several representative works, motif predictors such as “feed-forward-loop” and Bi-fan predictor perform well in both accuracy and efficiency. Nevertheless, they fail to explicitly explain the linkage motivation of nodes, nor do they consider the unequal contributions of different neighbors between node pairs. In this paper, motivated by the investment theory in economics, we propose a universal and explicable model to quantify the contributions of nodes on driving link formation. Based on the analysis on two typical investment relationships, namely “follow-up” and “co-follow”, an investment-profit index is designed for link prediction in directed networks. Empirical studies on 12 static networks and four temporal networks show that the proposed method outperforms eight mainstream baselines under three standard metrics. As a quasi-local index, it is also suitable for large-scale networks.

scholarly journals SNEQ: Semi-Supervised Attributed Network Embedding with Attention-Based Quantisation

2020 ◽  
Vol 34 (04) ◽  
pp. 4091-4098 ◽  
Author(s):  
Tao He ◽  
Lianli Gao ◽  
Jingkuan Song ◽  
Xin Wang ◽  
Kejie Huang ◽  
...  
Keyword(s):  
Link Prediction ◽  
Large Scale ◽  
State Of The Art ◽  
Network Embedding ◽  
Compression Method ◽  
Network Analytics ◽  
Attributed Network ◽  
Large Scale Networks ◽  
Low Dimensional ◽  
Embedding Methods

Learning accurate low-dimensional embeddings for a network is a crucial task as it facilitates many network analytics tasks. Moreover, the trained embeddings often require a significant amount of space to store, making storage and processing a challenge, especially as large-scale networks become more prevalent. In this paper, we present a novel semi-supervised network embedding and compression method, SNEQ, that is competitive with state-of-art embedding methods while being far more space- and time-efficient. SNEQ incorporates a novel quantisation method based on a self-attention layer that is trained in an end-to-end fashion, which is able to dramatically compress the size of the trained embeddings, thus reduces storage footprint and accelerates retrieval speed. Our evaluation on four real-world networks of diverse characteristics shows that SNEQ outperforms a number of state-of-the-art embedding methods in link prediction, node classification and node recommendation. Moreover, the quantised embedding shows a great advantage in terms of storage and time compared with continuous embeddings as well as hashing methods.

Covalent Atomic Bridges Enable Unidirectional Enhancement of Electronic Transport in Aligned Carbon Nanotubes

2019 ◽  
Author(s):  
Mingguang Chen ◽  
Wangxiang Li ◽  
Anshuman Kumar ◽  
Guanghui Li ◽  
Mikhail Itkis ◽  
...  
Keyword(s):  
Carbon Nanotubes ◽  
Electrical Transport ◽  
Large Scale ◽  
The Novel ◽  
Electrical Measurements ◽  
Metal Atoms ◽  
Aligned Carbon Nanotubes ◽  
Transport Channels ◽  
Walled Carbon Nanotubes ◽  
Bulk Structures

<p>Interconnecting the surfaces of nanomaterials without compromising their outstanding mechanical, thermal, and electronic properties is critical in the design of advanced bulk structures that still preserve the novel properties of their nanoscale constituents. As such, bridging the p-conjugated carbon surfaces of single-walled carbon nanotubes (SWNTs) has special implications in next-generation electronics. This study presents a rational path towards improvement of the electrical transport in aligned semiconducting SWNT films by deposition of metal atoms. The formation of conducting Cr-mediated pathways between the parallel SWNTs increases the transverse (intertube) conductance, while having negligible effect on the parallel (intratube) transport. In contrast, doping with Li has a predominant effect on the intratube electrical transport of aligned SWNT films. Large-scale first-principles calculations of electrical transport on aligned SWNTs show good agreement with the experimental electrical measurements and provide insight into the changes that different metal atoms exert on the density of states near the Fermi level of the SWNTs and the formation of transport channels. </p>

scholarly journals Documentary data and the study of past droughts: a global state of the art

2018 ◽  
Vol 14 (12) ◽  
pp. 1915-1960 ◽  
Author(s):  
Rudolf Brázdil ◽  
Andrea Kiss ◽  
Jürg Luterbacher ◽  
David J. Nash ◽  
Ladislava Řezníčková
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
Drought Indices ◽  
Documentary Evidence ◽  
Climatic Trends ◽  
Instrumental Observations ◽  
Spatio Temporal ◽  
Epigraphic Evidence ◽  
Administrative Evidence

Abstract. The use of documentary evidence to investigate past climatic trends and events has become a recognised approach in recent decades. This contribution presents the state of the art in its application to droughts. The range of documentary evidence is very wide, including general annals, chronicles, memoirs and diaries kept by missionaries, travellers and those specifically interested in the weather; records kept by administrators tasked with keeping accounts and other financial and economic records; legal-administrative evidence; religious sources; letters; songs; newspapers and journals; pictographic evidence; chronograms; epigraphic evidence; early instrumental observations; society commentaries; and compilations and books. These are available from many parts of the world. This variety of documentary information is evaluated with respect to the reconstruction of hydroclimatic conditions (precipitation, drought frequency and drought indices). Documentary-based drought reconstructions are then addressed in terms of long-term spatio-temporal fluctuations, major drought events, relationships with external forcing and large-scale climate drivers, socio-economic impacts and human responses. Documentary-based drought series are also considered from the viewpoint of spatio-temporal variability for certain continents, and their employment together with hydroclimate reconstructions from other proxies (in particular tree rings) is discussed. Finally, conclusions are drawn, and challenges for the future use of documentary evidence in the study of droughts are presented.

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

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