Social Network Analysis and Information Propagation: a Case Study Using Flickr and Youtube Networks
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A Survey of Network Performance Monitoring Tools
A Survey of Network Performance Monitoring Tools Travis Keshav -- [email protected] Abstract In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key. This paper analyzes several facets of Network Performance Monitoring, evaluating several motivations as well as examining many commercial and public domain products. Keywords: network performance monitoring, application monitoring, flow monitoring, packet capture, sniffing, wireless networks, path analysis, bandwidth analysis, network monitoring platforms, Ethereal, Netflow, tcpdump, Wireshark, Ciscoworks Table Of Contents 1. Introduction 2. Application & Host-Based Monitoring 2.1 Basis of Application & Host-Based Monitoring 2.2 Public Domain Application & Host-Based Monitoring Tools 2.3 Commercial Application & Host-Based Monitoring Tools 3. Flow Monitoring 3.1 Basis of Flow Monitoring 3.2 Public Domain Flow Monitoring Tools 3.3 Commercial Flow Monitoring Protocols 4. Packet Capture/Sniffing 4.1 Basis of Packet Capture/Sniffing 4.2 Public Domain Packet Capture/Sniffing Tools 4.3 Commercial Packet Capture/Sniffing Tools 5. Path/Bandwidth Analysis 5.1 Basis of Path/Bandwidth Analysis 5.2 Public Domain Path/Bandwidth Analysis Tools 6. Wireless Network Monitoring 6.1 Basis of Wireless Network Monitoring 6.2 Public Domain Wireless Network Monitoring Tools 6.3 Commercial Wireless Network Monitoring Tools 7. Network Monitoring Platforms 7.1 Basis of Network Monitoring Platforms 7.2 Commercial Network Monitoring Platforms 8. Conclusion 9. References and Acronyms 1.0 Introduction http://www.cse.wustl.edu/~jain/cse567-06/ftp/net_perf_monitors/index.html 1 of 20 In today's world of networks, it is not enough simply to have a network; assuring its optimal performance is key. -
Networkx: Network Analysis with Python
NetworkX: Network Analysis with Python Salvatore Scellato Full tutorial presented at the XXX SunBelt Conference “NetworkX introduction: Hacking social networks using the Python programming language” by Aric Hagberg & Drew Conway Outline 1. Introduction to NetworkX 2. Getting started with Python and NetworkX 3. Basic network analysis 4. Writing your own code 5. You are ready for your project! 1. Introduction to NetworkX. Introduction to NetworkX - network analysis Vast amounts of network data are being generated and collected • Sociology: web pages, mobile phones, social networks • Technology: Internet routers, vehicular flows, power grids How can we analyze this networks? Introduction to NetworkX - Python awesomeness Introduction to NetworkX “Python package for the creation, manipulation and study of the structure, dynamics and functions of complex networks.” • Data structures for representing many types of networks, or graphs • Nodes can be any (hashable) Python object, edges can contain arbitrary data • Flexibility ideal for representing networks found in many different fields • Easy to install on multiple platforms • Online up-to-date documentation • First public release in April 2005 Introduction to NetworkX - design requirements • Tool to study the structure and dynamics of social, biological, and infrastructure networks • Ease-of-use and rapid development in a collaborative, multidisciplinary environment • Easy to learn, easy to teach • Open-source tool base that can easily grow in a multidisciplinary environment with non-expert users -
An Introduction
This chapter is from Social Media Mining: An Introduction. By Reza Zafarani, Mohammad Ali Abbasi, and Huan Liu. Cambridge University Press, 2014. Draft version: April 20, 2014. Complete Draft and Slides Available at: http://dmml.asu.edu/smm Chapter 10 Behavior Analytics What motivates individuals to join an online group? When individuals abandon social media sites, where do they migrate to? Can we predict box office revenues for movies from tweets posted by individuals? These questions are a few of many whose answers require us to analyze or predict behaviors on social media. Individuals exhibit different behaviors in social media: as individuals or as part of a broader collective behavior. When discussing individual be- havior, our focus is on one individual. Collective behavior emerges when a population of individuals behave in a similar way with or without coordi- nation or planning. In this chapter we provide examples of individual and collective be- haviors and elaborate techniques used to analyze, model, and predict these behaviors. 10.1 Individual Behavior We read online news; comment on posts, blogs, and videos; write reviews for products; post; like; share; tweet; rate; recommend; listen to music; and watch videos, among many other daily behaviors that we exhibit on social media. What are the types of individual behavior that leave a trace on social media? We can generally categorize individual online behavior into three cate- gories (shown in Figure 10.1): 319 Figure 10.1: Individual Behavior. 1. User-User Behavior. This is the behavior individuals exhibit with re- spect to other individuals. For instance, when befriending someone, sending a message to another individual, playing games, following, inviting, blocking, subscribing, or chatting, we are demonstrating a user-user behavior. -
Three-Dimensional Integrated Circuit Design: EDA, Design And
Integrated Circuits and Systems Series Editor Anantha Chandrakasan, Massachusetts Institute of Technology Cambridge, Massachusetts For other titles published in this series, go to http://www.springer.com/series/7236 Yuan Xie · Jason Cong · Sachin Sapatnekar Editors Three-Dimensional Integrated Circuit Design EDA, Design and Microarchitectures 123 Editors Yuan Xie Jason Cong Department of Computer Science and Department of Computer Science Engineering University of California, Los Angeles Pennsylvania State University [email protected] [email protected] Sachin Sapatnekar Department of Electrical and Computer Engineering University of Minnesota [email protected] ISBN 978-1-4419-0783-7 e-ISBN 978-1-4419-0784-4 DOI 10.1007/978-1-4419-0784-4 Springer New York Dordrecht Heidelberg London Library of Congress Control Number: 2009939282 © Springer Science+Business Media, LLC 2010 All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Foreword We live in a time of great change. -
Learning to Predict End-To-End Network Performance
CORE Metadata, citation and similar papers at core.ac.uk Provided by BICTEL/e - ULg UNIVERSITÉ DE LIÈGE Faculté des Sciences Appliquées Institut d’Électricité Montefiore RUN - Research Unit in Networking Learning to Predict End-to-End Network Performance Yongjun Liao Thèse présentée en vue de l’obtention du titre de Docteur en Sciences de l’Ingénieur Année Académique 2012-2013 ii iii Abstract The knowledge of end-to-end network performance is essential to many Internet applications and systems including traffic engineering, content distribution networks, overlay routing, application- level multicast, and peer-to-peer applications. On the one hand, such knowledge allows service providers to adjust their services according to the dynamic network conditions. On the other hand, as many systems are flexible in choosing their communication paths and targets, knowing network performance enables to optimize services by e.g. intelligent path selection. In the networking field, end-to-end network performance refers to some property of a network path measured by various metrics such as round-trip time (RTT), available bandwidth (ABW) and packet loss rate (PLR). While much progress has been made in network measurement, a main challenge in the acquisition of network performance on large-scale networks is the quadratical growth of the measurement overheads with respect to the number of network nodes, which ren- ders the active probing of all paths infeasible. Thus, a natural idea is to measure a small set of paths and then predict the others where there are no direct measurements. This understanding has motivated numerous research on approaches to network performance prediction. -
Designing Vertical Bandwidth Reconfigurable 3D Nocs for Many
Designing Vertical Bandwidth Reconfigurable 3D NoCs for Many Core Systems Qiaosha Zou∗, Jia Zhany, Fen Gez, Matt Poremba∗, Yuan Xiey ∗ Computer Science and Engineering, The Pennsylvania State University, USA y Electrical and Computer Engineering, University of California, Santa Barbara, USA z Nanjing University of Aeronautics and Astronautics, China Email: ∗fqszou, [email protected], yfjzhan, [email protected], z [email protected] Abstract—As the number of processing elements increases in a single Routers chip, the interconnect backbone becomes more and more stressed when Core 1 Cache/ Cache/Memory serving frequent memory and cache accesses. Network-on-Chip (NoC) Memory has emerged as a potential solution to provide a flexible and scalable Core 0 interconnect in a planar platform. In the mean time, three-dimensional TSVs (3D) integration technology pushes circuit design beyond Moore’s law and provides short vertical connections between different layers. As a result, the innovative solution that combines 3D integrations and NoC designs can further enhance the system performance. However, due Core 0 Core 1 to the unpredictable workload characteristics, NoC may suffer from intermittent congestions and channel overflows, especially when the (a) (b) network bandwidth is limited by the area and energy budget. In this work, we explore the performance bottlenecks in 3D NoC, and then leverage Fig. 1. Overview of core-to-cache/memory 3D stacking. (a). Cores are redundant TSVs, which are conventionally used for fault tolerance only, allocated in all layers with part of cache/memory; (b). All cores are located as vertical links to provide additional channel bandwidth for instant in the same layers while cache/memory in others. -
A Social Network Matrix for Implicit and Explicit Social Network Plates
A Social Network Matrix for Implicit and Explicit Social Network Plates Wei Zhou1;4, Wenjing Duan2, Selwyn Piramuthu3;4 1Information & Operations Management, ESCP Europe, Paris, France 2Information Systems and Technology Management, George Washington University, U.S.A. 3Information Systems and Operations Management, University of Florida, U.S.A. Gainesville, Florida 32611-7169, USA 4RFID European Lab, Paris, France. [email protected], [email protected], selwyn@ufl.edu Abstract A majority of social network research deals with explicitly formed social networks. Although only rarely acknowledged for its existence, we believe that implicit social networks play a significant role in the overall dynamics of social networks. We propose a framework to evalu- ate the dynamics and characteristics of a set of explicit and associated implicit social networks. Specifically, we propose a social network ma- trix to measure the implicit relationships among the entities in various social networks. We also derive several indicators to characterize the dynamics in online social networks. We proceed by incorporating im- plicit social networks in a traditional network flow context to evaluate key network performance indicators such as the lowest communication cost, maximum information flow, and the budgetary constraints. Keywords: Implicit Social Network, Online Social Network 1 1 Introduction In recent years, several online social network platforms have witnessed huge public attention from social and financial perspectives. However, there are different facets to this interest. Facebook, for example, has gained a large set of users but failed to excel on profitability. Online social networks can be broadly classified as explicit and implicit social networks. Explicit social networks (e.g., Facebook, LinkedIn, Twitter, and MySpace) are where the users define the network by explicitly connect- ing with other users, possibly, but not necessarily, based on shared interests. -
Network Performance Analysis
Network Performance Analysis Guido Sanguinetti (based on notes by Neil Lawrence) Autumn Term 2007-2008 2 Schedule Lectures will take place in the following locations: Mondays 15:10 - 17:00 pm (St Georges LT1). Tuesdays 16:10 pm - 17:00 pm (St Georges LT1). Lab Classes In the Lewin Lab, Regent Court G12 (Monday Weeks 3 and 8). Week 1 Monday Lectures 1 & 2 Networks and Probability Review Tuesday Lecture 3 Poisson Processes Week 2 Monday Lecture 4 & 5 Birth Death Processes, M=M=1 queues Tuesday Lecture 6 Little's Formula Week 3 Monday Lab Class 1 Discrete Simulation of the M=M=1 Queue Tuesday Tutorial 1 Probability and Poisson Processes Week 4 Monday Lectures 7 & 8 Other Markov Queues and Erlang Delay Tuesday Lab Review 1 Review of Lab Class 1 Week 5 Monday Lectures 9 & 10 Erlang Loss and Markov Queues Review Tuesday Tutorial 2 M=M=1 and Little's Formula Week 6 Monday Lectures 11 & 12 M=G=1 and M=G=1 with Vacations Tuesday Lecture 13 M=G=1 contd Week 7 Monday Lectures 14 & 15 Networks of Queues, Jackson's Theorem Tuesday Tutorial 3 Erlang Delay and Loss, M=G=1 Week 8 Monday Lab Class 2 Simulation of Queues in NS2 Tuesday Lecture 16 ALOHA Week 9 Tuesday Lab Review 2 Review of NS2 Lab Class Week 10 Exam Practice Practice 2004-2005 Exam Week 12 Monday Exam Review Review of 2004-2005 Exam Evaluation This course is 100% exam. 3 4 Reading List The course is based primarily on Bertsekas and Gallager [1992]. -
A Framework for a Bandwidth Based Network Performance Model for CS Students
A Framework for a Bandwidth Based Network Performance Model for CS Students D. Veal, G. Kohli, S. P. Maj J. Cooper Edith Cowan University Curtin University of Technology Western Australia Western Australia [email protected] Abstract There are currently various methods by which network and internetwork performance can be addressed. Examples include simulation modeling and analytical modeling which often results in models that are highly complex and often mathematically based (e.g. queuing theory). The authors have developed a new model which is based upon simple formulae derived from an investigation of computer networks. This model provides a conceptual framework for performance analysis based upon bandwidth considerations from a constructivist perspective. The bandwidth centric model uses high level abstraction decoupled from the implementation details of the underlying technology. This paper represents an initial attempt to develop this theory further in the field of networking education and presents a description of some of our work to date. This paper also includes details of experiments undertaken to measure bandwidth and formulae derived and applied to the investigation of converging data streams. Introduction There are many ways of approaching computer networking education. Davies notes that: “Network courses are often based on one or more of the following areas: The OSI model; Performance analysis; and Network simulation” 1. The OSI model is a popular approach that is used extensively in the Cisco Networking Academy Program (CNAP) 2 and in other Cisco learning materials. With respect to simulation Davis describes the Optimized Network Engineering Tools (OPNET) system that that can model networks and sub-networks, individual nodes and stations and state transition models that defines a node 1. -
Deep Generative Modeling in Network Science with Applications to Public Policy Research
Working Paper Deep Generative Modeling in Network Science with Applications to Public Policy Research Gavin S. Hartnett, Raffaele Vardavas, Lawrence Baker, Michael Chaykowsky, C. Ben Gibson, Federico Girosi, David Kennedy, and Osonde Osoba RAND Health Care WR-A843-1 September 2020 RAND working papers are intended to share researchers’ latest findings and to solicit informal peer review. They have been approved for circulation by RAND Health Care but have not been formally edted. Unless otherwise indicated, working papers can be quoted and cited without permission of the author, provided the source is clearly referred to as a working paper. RAND’s R publications do not necessarily reflect the opinions of its research clients and sponsors. ® is a registered trademark. CORPORATION For more information on this publication, visit www.rand.org/pubs/working_papers/WRA843-1.html Published by the RAND Corporation, Santa Monica, Calif. © Copyright 2020 RAND Corporation R® is a registered trademark Limited Print and Electronic Distribution Rights This document and trademark(s) contained herein are protected by law. This representation of RAND intellectual property is provided for noncommercial use only. Unauthorized posting of this publication online is prohibited. Permission is given to duplicate this document for personal use only, as long as it is unaltered and complete. Permission is required from RAND to reproduce, or reuse in another form, any of its research documents for commercial use. For information on reprint and linking permissions, please visit www.rand.org/pubs/permissions.html. The RAND Corporation is a research organization that develops solutions to public policy challenges to help make communities throughout the world safer and more secure, healthier and more prosperous. -
Random Graph Models of Social Networks
Colloquium Random graph models of social networks M. E. J. Newman*†, D. J. Watts‡, and S. H. Strogatz§ *Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501; ‡Department of Sociology, Columbia University, 1180 Amsterdam Avenue, New York, NY 10027; and §Department of Theoretical and Applied Mechanics, Cornell University, Ithaca, NY 14853-1503 We describe some new exactly solvable models of the structure of actually connected by a very short chain of intermediate social networks, based on random graphs with arbitrary degree acquaintances. He found this chain to be of typical length of distributions. We give models both for simple unipartite networks, only about six, a result which has passed into folklore by means such as acquaintance networks, and bipartite networks, such as of John Guare’s 1990 play Six Degrees of Separation (10). It has affiliation networks. We compare the predictions of our models to since been shown that many networks have a similar small- data for a number of real-world social networks and find that in world property (11–14). some cases, the models are in remarkable agreement with the data, It is worth noting that the phrase ‘‘small world’’ has been used whereas in others the agreement is poorer, perhaps indicating the to mean a number of different things. Early on, sociologists used presence of additional social structure in the network that is not the phrase both in the conversational sense of two strangers who captured by the random graph. discover that they have a mutual friend—i.e., that they are separated by a path of length two—and to refer to any short path social network is a set of people or groups of people, between individuals (8, 9). -
Navigating Networks by Using Homophily and Degree
Navigating networks by using homophily and degree O¨ zgu¨r S¸ ims¸ek* and David Jensen Department of Computer Science, University of Massachusetts, Amherst, MA 01003 Edited by Peter J. Bickel, University of California, Berkeley, CA, and approved July 11, 2008 (received for review January 16, 2008) Many large distributed systems can be characterized as networks We show that, across a wide range of synthetic and real-world where short paths exist between nearly every pair of nodes. These networks, EVN performs as well as or better than the best include social, biological, communication, and distribution net- previous algorithms. More importantly, in the majority of cases works, which often display power-law or small-world structure. A where previous algorithms do not perform well, EVN synthesizes central challenge of distributed systems is directing messages to whatever homophily and degree information can be exploited to specific nodes through a sequence of decisions made by individual identify much shorter paths than any previous method. nodes without global knowledge of the network. We present a These results have implications for understanding the func- probabilistic analysis of this navigation problem that produces a tioning of current and prospective distributed systems that route surprisingly simple and effective method for directing messages. messages by using local information. These systems include This method requires calculating only the product of the two social networks routing messages, referral systems for finding measures widely used to summarize all local information. It out- informed experts, and also technological systems for routing performs prior approaches reported in the literature by a large messages on the Internet, ad hoc wireless networking, and margin, and it provides a formal model that may describe how peer-to-peer file sharing.