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Toward online node classification on streaming networks

Ling Jian, Jundong Li, Huan Liu
2017 Data mining and knowledge discovery  
Considering the streaming nature of networks, we study how to perform online node classification on this kind of streaming networks (a.k.a. online learning on streaming networks).  ...  Theoretical analysis is presented to show the superiority of the proposed framework of online learning on streaming networks (OLSN).  ...  One straightforward way to perform online node classification for the streaming network is to apply traditional node classification methods in an offline way each time when some new nodes arrive.  ... 
doi:10.1007/s10618-017-0533-y fatcat:fxttc2qlmzgshexctkmfbg5rju

Cooperative Learning for Distributed In-Network Traffic Classification

S.B. Joseph, H.R. Loo, I. Ismail, T. Andromeda, M.N. Marsono
2017 IOP Conference Series: Materials Science and Engineering  
In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification.  ...  Classification of weakly commutative complex homogeneous spaces Ivan V Losev -This content was downloaded from IP address 207.241.231.83 on 25/07 Abstract.  ...  This paper proposes an analysis for online distributed in-network traffic classification based on our CL framework in [5] , using incremental k-means network traffic classification [1] .  ... 
doi:10.1088/1757-899x/190/1/012010 fatcat:3xwtkp55q5fwve2podtwq4po2e

Cooperative Learning for Distributed In-Network Traffic Classification

S.B. Joseph, H.R. Loo, I. Ismail, T. Andromeda, M.N. Marsono
2016 Proceeding of the Electrical Engineering Computer Science and Informatics  
In this paper, we propose a cooperative learning algorithm for propagation and synchronization of network information among autonomic distributed network nodes for online traffic classification.  ...  The overall performance indicates that cooperative learning is promising for distributed in-network traffic classification.  ...  This paper proposes an analysis for online distributed in-network traffic classification based on our CL framework in [5] , using incremental k-means network traffic classification [1] .  ... 
doi:10.11591/eecsi.v3i1.1144 fatcat:xaa2oacxbngcbj57btln6hqjmq

Preface to the Special Issue on Graph Data Management in Online Social Networks

Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, Junping Du
2020 World wide web (Bussum)  
We are delighted to present this special issue of World Wide Web on Graph Data Management in Online Social Networks.  ...  Graph data management, which concerns techniques in modelling, storing, querying, and learning graph data has been found particular useful in online social network (OSN) analysis, such as expert finding  ...  semi-structured data in the classification labels of web pages, and extract non-superordinate relationships from unstructured text through the proposed convolution residual network based on improved cross-entropy  ... 
doi:10.1007/s11280-019-00771-0 fatcat:ee7b2an4xzc77clj2w36oyl4zq

DYNG: Dynamic Online Growing Neural Gas for stream data classification

Oliver Beyer, Philipp Cimiano
2013 The European Symposium on Artificial Neural Networks  
In this paper we introduce Dynamic Online Growing Neural Gas (DYNG), a novel online stream data classification approach based on Online Growing Neural Gas (OGNG).  ...  DYNG exploits labelled data during processing to adapt the network structure as well as the speed of growth of the network to the requirements of the classification task.  ...  We have successfully applied the algorithm on two stream data sets in a life-long learning scenario and shown that it improves upon an existing online classification algorithm based on GNG, i.e.  ... 
dblp:conf/esann/BeyerC13 fatcat:7jcp3rsm6vcxnci7lhx2brtpha

Online computational ethology based on modern IT infrastructure

Leon B. Larsen, Mathias M. Neerup, John Hallam
2021 Ecological Informatics  
In this work, we discuss the requirements and challenges for such a system and propose an implementation based on modern IT infrastructure.  ...  As more and more methods and algorithms are developed we expect online systems to enable new experimental setups to study behaviour, leading to new insights in the field.  ...  Acknowledgements We want to thank Thor Andreasen for letting us use one of his recording arrays for the experiments, Sara Sofie Thagaard Winther and Odense Zoo for testing our system, Cao Danh Do for manufacturing  ... 
doi:10.1016/j.ecoinf.2021.101290 fatcat:m34rfrauhzfpdpqm3ssbwmcet4

An investigation of the hoeffding adaptive tree for the problem of network intrusion detection

Diego Guarnieri Correa, Fabricio Enembreck, Carlos N. Silla
2017 2017 International Joint Conference on Neural Networks (IJCNN)  
In this paper we have proposed an adaptive anomaly intrusion detection model using stream mining approach which identifies the changes in the network quickly and adapts the underlying model.  ...  We have used Adaptive Size Hoeffding tree, online boosting algorithm and an adaptive sliding window algorithm ADWIN in our model.  ...  On these streams of incoming data, online from each other.  ... 
doi:10.1109/ijcnn.2017.7966369 dblp:conf/ijcnn/CorreaES17 fatcat:m5fwhdvsdjhcfkaaym7aw6crkq

Finding Optimal Random Boolean Networks for Reservoir Computing

David Snyder, Alireza Goudarzi, Christof Teuscher
2012 Artificial Life 13  
In this paper, we use Random Boolean Networks (RBN) to build the reservoir.  ...  We explore the computational capabilities of such a RC device using the temporal parity task and the temporal density classification.  ...  Formally, a RBN is a collection of N such binary nodes. For each node i out of N nodes, the node receives K i inputs, each of which is connected to one of the N nodes in the network.  ... 
doi:10.7551/978-0-262-31050-5-ch035 dblp:conf/alife/SnyderGT12 fatcat:cxvfpf3tqvd6ri2r5tigxxtdqy

Network Sampling: From Static to Streaming Graphs [article]

Nesreen K. Ahmed and Jennifer Neville and Ramana Kompella
2012 arXiv   pre-print
Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.  ...  Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling.  ...  wide web (WWW), and online social networks (OSN).  ... 
arXiv:1211.3412v1 fatcat:4k3vrxwe65h3nisl323d27qeby

EVFDT: An Enhanced Very Fast Decision Tree Algorithm for Detecting Distributed Denial of Service Attack in Cloud-Assisted Wireless Body Area Network

Rabia Latif, Haider Abbas, Seemab Latif, Ashraf Masood
2015 Mobile Information Systems  
To analyze the performance of EVFDT, four metrics are evaluated: classification accuracy, tree size, time, and memory.  ...  To overcome these shortcomings, Very Fast Decision Tree (VFDT) algorithm has been proposed in the past that can handle high speed streaming data efficiently.  ...  The comparison will tell us that this leaf node has less contribution towards classification as a smaller number of instances are filtered to this leaf node on the current HT.  ... 
doi:10.1155/2015/260594 fatcat:up5kz3dmlfbarollnu6hvnv7wa

User Quality of Experience (QoE) Satisfaction for Video Content Selection (VCS) Framework in Smartphone Devices

Muhamad Hanif Jofri, Ida Aryanie Bahrudin, Noor Zuraidin Mohd Safar, Juliana Mohamed, Abdul Halim Omar
2021 Baghdad Science Journal  
To evaluate the satisfaction level, we used the Mean Opinion Score (MOS) to measure the adaptability of user acceptance towards video streaming quality.  ...  However, when streaming in session, user Quality of Experience (QoE) is unsatisfied with the video content selection while streaming on smartphone devices.  ...  Selecting video quality depending on user QoE take both process adaptation of network stability and user QoE towards video selection.  ... 
doi:10.21123/bsj.2021.18.4(suppl.).1387 fatcat:bjl3why4yzhq5e4augu3bjaijm

Outlier Detection Strategies for WSNs: A Survey

Bhanu Chander, G. Kumaravelan
2021 Journal of King Saud University: Computer and Information Sciences  
Wireless Sensor Networks (WSNs) are developed significantly from the last decades and attracted the attention of scientific and industrial domains.  ...  In WSNs, sensor nodes distributed autonomously in harsh environments are easily vulnerable to faults and attacks that cause sensor readings unreliable and inaccurate.  ...  Later, sink approximately figure out peak n global abnormal streams and these recurrent networks remain as a proof to entire deployed nodes.  ... 
doi:10.1016/j.jksuci.2021.02.012 fatcat:rpgswasszzbgdbkziskhqrqjam

Network Sampling

Nesreen K. Ahmed, Jennifer Neville, Ramana Kompella
2013 ACM Transactions on Knowledge Discovery from Data  
Finally, we study the impact of network sampling algorithms on the parameter estimation and performance evaluation of relational classification algorithms.  ...  Furthermore, we demonstrate how traditional static sampling algorithms can be modified for graph streams for each of the three main classes of sampling methods: node, edge, and topology-based sampling.  ...  wide web (WWW), and online social networks (OSN).  ... 
doi:10.1145/2601438 fatcat:zri4akt2lfcazbw36syvj5ru7i

Practical Application of Machine Learning based Online Intrusion Detection to Internet of Things Networks

Christopher Nixon, Mohamed Sedky, Mohamed Hassan
2019 2019 IEEE Global Conference on Internet of Things (GCIoT)  
Future research should focus on addressing class imbalance in the data streams to ensure that minority attack classes are not missed.  ...  An additional challenge is that IoT networks are a continuous non-stationary data stream that, due to their variable nature, are likely to experience concept drift.  ...  Here a time and memory efficient, informed online learning approach, such as NB with HDDM, is used on IoT nodes to perform misuse detection.  ... 
doi:10.1109/gciot47977.2019.9058410 fatcat:4xg4cuxmsvguzjea7qch6t3tvm

Automatic Online Multi-Source Domain Adaptation [article]

Renchunzi Xie, Mahardhika Pratama
2021 arXiv   pre-print
An online domain adaptation technique under multisource streaming processes, namely automatic online multi-source domain adaptation (AOMSDA), is proposed in this paper.  ...  of data streams.  ...  Low gap between precision and recall means unbiased prediction toward one of the classes.  ... 
arXiv:2109.01996v2 fatcat:2xkne7rltncjpjrrg2nfq447vu
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