Node (physics)
View article: MEGA11: Molecular Evolutionary Genetics Analysis Version 11
MEGA11: Molecular Evolutionary Genetics Analysis Version 11 Open
The Molecular Evolutionary Genetics Analysis (MEGA) software has matured to contain a large collection of methods and tools of computational molecular evolution. Here, we describe new additions that make MEGA a more comprehensive tool for …
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Using Embeddings to Improve Named Entity Recognition Classification with Graphs Open
Richer information has potential to improve performance of NLP (Natural Language Processing) tasks such as Named Entity Recognition. A linear sequence of words can be enriched with the sentence structure, as well as their syntactic structu…
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<span>ggtree</span> : an <span>r</span> package for visualization and annotation of phylogenetic trees with their covariates and other associated data Open
Summary We present an r package, ggtree , which provides programmable visualization and annotation of phylogenetic trees. ggtree can read more tree file formats than other softwares, including newick , nexus , NHX , phylip and jplace forma…
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Inductive Representation Learning on Large Graphs Open
Low-dimensional embeddings of nodes in large graphs have proved extremely useful in a variety of prediction tasks, from content recommendation to identifying protein functions. However, most existing approaches require that all nodes in th…
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Interactive Tree of Life (iTOL) v6: recent updates to the phylogenetic tree display and annotation tool Open
The Interactive Tree Of Life (https://itol.embl.de) is an online tool for the management, display, annotation and manipulation of phylogenetic and other trees. It is freely available and open to everyone. iTOL version 6 introduces a modern…
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Identifying highly influential nodes in the complicated grief network. Open
The network approach to psychopathology conceptualizes mental disorders as networks of mutually reinforcing nodes (i.e., symptoms). Researchers adopting this approach have suggested that network topology can be used to identify influential…
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Heterogeneous Graph Neural Network Open
Representation learning in heterogeneous graphs aims to pursue a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. Thi…
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Cluster-GCN Open
Graph convolutional network (GCN) has been successfully applied to many\ngraph-based applications; however, training a large-scale GCN remains\nchallenging. Current SGD-based algorithms suffer from either a high\ncomputational cost that ex…
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What do centrality measures measure in psychological networks? Open
Centrality indices are a popular tool to analyze structural aspects of psychological networks. As centrality indices were originally developed in the context of social networks, it is unclear to what extent these indices are suitable in a …
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<i>struc2vec</i> Open
Structural identity is a concept of symmetry in which network nodes are\nidentified according to the network structure and their relationship to other\nnodes. Structural identity has been studied in theory and practice over the\npast decad…
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MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding Open
A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous gra…
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Learning Convolutional Neural Networks for Graphs Open
Numerous important problems can be framed as learning from graph data. We propose a framework for learning convolutional neural networks for arbitrary graphs. These graphs may be undirected, directed, and with both discrete and continuous …
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Update or Wait: How to Keep Your Data Fresh Open
In this paper, we study how to optimally manage the freshness of infimmation updates sent from a source node to a destination via a channel. A proper metric for data freshness at the destination is the age-of-information, or simply age, wh…
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UAV-Based IoT Platform: A Crowd Surveillance Use Case Open
Unmanned aerial vehicles (UAVs) are used to provide diverse civilian, commercial, and governmental services. In addition to their original tasks, UAVs can also be used to offer numerous value-added internet of things services (VAIoTSs). Sh…
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Representation Learning on Graphs with Jumping Knowledge Networks Open
Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of …
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Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting Open
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an evolving traffic system. Recent works focus on designing c…
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GNNExplainer: Generating Explanations for Graph Neural Networks Open
Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating bo…
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification Open
\emph{Over-fitting} and \emph{over-smoothing} are two main obstacles of developing deep Graph Convolutional Networks (GCNs) for node classification. In particular, over-fitting weakens the generalization ability on small dataset, while ove…
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End-to-End Structure-Aware Convolutional Networks for Knowledge Base Completion Open
Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over e…
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Protein interface prediction using graph convolutional networks Open
Proteins play a critical role in processes both within and between cells, through their interactions with each other and other molecules. Proteins interact via an interface forming a protein complex, which is difficult, expensive, and time…
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RSSI-Based Indoor Localization With the Internet of Things Open
In the era of smart cities, there are a plethora of applications where the localization of indoor environments is important, from monitoring and tracking in smart buildings to proximity marketing and advertising in shopping malls. The succ…
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MolGAN: An implicit generative model for small molecular graphs Open
Deep generative models for graph-structured data offer a new angle on the problem of chemical synthesis: by optimizing differentiable models that directly generate molecular graphs, it is possible to side-step expensive search procedures i…
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Anchor-free Distributed Localization in Sensor Networks Open
Many sensor network applications require that each node's sensor stream be annotated with its physical location in some common coordinate system. Manual measurement and configuration methods for obtaining location don't scale and are error…
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The ground truth about metadata and community detection in networks Open
Troubles with community detection in networks: No ground truth, no free lunch, and the complex coupling of metadata with structure.
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Attributed Social Network Embedding Open
Embedding network data into a low-dimensional vector space has shown promising performance for many real-world applications, such as node classification and entity retrieval. However, most existing methods focused only on leveraging networ…
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A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements Open
Software defined networking (SDN) brings about innovation, simplicity in network management, \nand configuration in network computing. Traditional networks often lack the flexibility to bring into \neffect instant changes because of the ri…
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Representation Learning for Attributed Multiplex Heterogeneous Network Open
Network embedding (or graph embedding) has been widely used in many\nreal-world applications. However, existing methods mainly focus on networks\nwith single-typed nodes/edges and cannot scale well to handle large networks.\nMany real-worl…
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Beyond Low-frequency Information in Graph Convolutional Networks Open
Graph neural networks (GNNs) have been proven to be effective in various network-related tasks. Most existing GNNs usually exploit the low-frequency signals of node features, which gives rise to one fundamental question: is the low-frequen…
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Deep Anomaly Detection on Attributed Networks Open
Attributed networks are ubiquitous and form a critical component of modern information infrastructure, where additional node attributes complement the raw network structure in knowledge discovery. Recently, detecting anomalous nodes on att…
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Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution Open
Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods a…