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SOCNET  February 2016

SOCNET February 2016

Subject:

selected Latest Complexity Digest Posts (fwd)

From:

Barry Wellman <[log in to unmask]>

Reply-To:

Barry Wellman <[log in to unmask]>

Date:

Mon, 8 Feb 2016 10:20:18 -0500

Content-Type:

MULTIPART/MIXED

Parts/Attachments:

Parts/Attachments

TEXT/PLAIN (181 lines)

*****  To join INSNA, visit http://www.insna.org  *****

   Barry Wellman
    A vision is just a vision if it's only in your head
    Step by step, link by link, putting it together
                  Streisand/Sondheim
  _______________________________________________________________________
   Visiting Prof         Schl of Information        University of Arizona
   NetLab Network                 FRSC                      INSNA Founder
   http://www.chass.utoronto.ca/~wellman           twitter: @barrywellman
   NETWORKED:The New Social Operating System   Lee Rainie & Barry Wellman
   MIT Press            http://amzn.to/zXZg39       Print $18  Kindle $11
   _______________________________________________________________________

selections
---------- Forwarded message ----------
Date: Mon, 8 Feb 2016 12:04:30 +0000
From: "[utf-8] Complexity Digest" <[log in to unmask]>
Reply-To: [log in to unmask]
To: "[utf-8] Barry" <[log in to unmask]>
Subject: [utf-8] Latest Complexity Digest Posts

Learn about the latest and greatest related to complex systems research. More at http://unam.us4.list-manage.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=364887499d&e=55e25a0e3e

Sentiment analysis and the complex natural language

    There is huge amount of content produced online by amateur authors, 
covering a large variety of topics. Sentiment analysis (SA) extracts and 
aggregates users˙˙ sentiments towards a target entity. Machine learning 
(ML) techniques are frequently used as the natural language data is in 
abundance and has definite patterns. ML techniques adapt to domain 
specific solution at high accuracy depending upon the feature set used. 
The lexicon-based techniques, using external dictionary, are independent 
of data to prevent overfitting but they miss context too in specialized 
domains. Corpus-based statistical techniques require large data to 
stabilize. Complex network based techniques are highly resourceful, 
preserving order, proximity, context and relationships. Recent 
applications developed incorporate the platform specific structural 
information i.e. meta-data. New sub-domains are introduced as influence 
analysis, bias analysis, and data leakage analysis. The nature of data is 
also evolving where transcribed customer-agent phone conversation are also 
used for sentiment analysis. This paper reviews sentiment analysis 
techniques and highlight the need to address natural language processing 
(NLP) specific open challenges. Without resolving the complex NLP 
challenges, ML techniques cannot make considerable advancements. The open 
issues and challenges in the area are discussed, stressing on the need of 
standard datasets and evaluation methodology. It also emphasized on the 
need of better language models that could capture context and proximity.

Sentiment analysis and the complex natural language
Muhammad Taimoor Khan, Mehr Durrani, Armughan Ali, Irum Inayat, Shehzad Khalid and Kamran Habib Khan

Complex Adaptive Systems Modeling20164:2
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Disentangling the Effects of Social Signals

    In peer recommendation systems, social signals affect item popularity about half as much as position and content do, and further create a "herding" effect that biases people's judgments about the content.

Disentangling the Effects of Social Signals

Tad Hogg & Kristina Lerman ·

Human Computation 2(2)

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Introduction to the modeling and analysis of complex systems: a review

    Hiroki Sayama˙˙s book ˙˙Introduction to the Modeling and Simulation of Complex Systems˙˙ is therefore a unique and welcome addition to any instructor˙˙s collection. What makes it valuable is that it not only presents a state-of-the-art review of the domain but also serves as a gentle guide to learning the sophisticated art of modeling complex systems.
The book is primarily composed of three types of chapters: preliminary chapters followed by logically interspersed modeling and analysis chapters. It has been designed for use both in basic as well as advanced courses spanning 1˙˙2 semesters. Additionally, the book demonstrates the use of PyCX, a freely available Python-based complex systems simulation framework

Introduction to the modeling and analysis of complex systems: a review
Muaz A. Niazi

Complex Adaptive Systems Modeling20164:3
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System under large stress: Prediction and management of catastrophic failures

    The tensile strength of a chain is determined by its weakest link. Does this idea apply to more complex systems too? For instance, does the weakest thread of a spider web initiate cascading failure, when a strong wind gust is stretching the web to its limit? What happens to a computer when both the supply voltage and the ambient temperature are more than 20% outside its normal range of operations?
Climate change, an increasingly more densely populated world and the rapid change of technology seem to put more systems under large stress. Engineering sustainable systems with a more favorable response to large stress appears to be an urgent societal need. Emergency evacuations of hospitals after hurricane Katharina and Sandy, and the May 22, 2011 tornado in Joplin illustrate the urgent need for modeling the adaptive capacity of hospitals during an extended loss of infrastructure [1]. Presidential Policy Directive 21 [2] and the U.S. Department of Homeland Security National Infrastructure Protection Plan (NIPP) [3] call for increasing resilience of the nation˙˙s critical infrastructure.

System under large stress: Prediction and management of catastrophic failures
Alfred Hübler

Complexity
Volume 21, Issue 3, pages 9˙˙12, January/February 2016

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Innovation diffusion on time-varying activity driven networks

    Since its introduction in the 1960s, the theory of innovation diffusion has contributed to the advancement of several research fields, such as marketing management and consumer behavior. The 1969 seminal paper by Bass [F.M. Bass, Manag. Sci. 15, 215 (1969)] introduced a model of product growth for consumer durables, which has been extensively used to predict innovation diffusion across a range of applications. Here, we propose a novel approach to study innovation diffusion, where interactions among individuals are mediated by the dynamics of a time-varying network. Our approach is based on the Bass˙˙ model, and overcomes key limitations of previous studies, which assumed timescale separation between the individual dynamics and the evolution of the connectivity patterns. Thus, we do not hypothesize homogeneous mixing among individuals or the existence of a fixed interaction network. We formulate our approach in the framework of activity driven networks to enable the analysis of
the concurrent evolution of the interaction and individual dynamics. Numerical simulations offer a systematic analysis of the model behavior and highlight the role of individual activity on market penetration when targeted advertisement campaigns are designed, or a competition between two different products takes place.

Innovation diffusion on time-varying activity driven networks
Alessandro Rizzo , Maurizio Porfiri

The European Physical Journal B
January 2016, 89:20

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A Biologically Inspired Model of Distributed Online Communication Supporting Efficient Search and Diffusion of Innovation

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Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks

    Women are dramatically underrepresented in computer science at all levels in academia and account for just 15% of tenure-track faculty. Understanding the causes of this gender imbalance would inform both policies intended to rectify it and employment decisions by departments and individuals. Progress in this direction, however, is complicated by the complexity and decentralized nature of faculty hiring and the non-independence of hires. Using comprehensive data on both hiring outcomes and scholarly productivity for 2659 tenure-track faculty across 205 Ph.D.-granting departments in North America, we investigate the multi-dimensional nature of gender inequality in computer science faculty hiring through a network model of the hiring process. Overall, we find that hiring outcomes are most directly affected by (i) the relative prestige between hiring and placing institutions and (ii) the scholarly productivity of the candidates. After including these, and other features, the
addition of gender did not significantly reduce modeling error. However, gender differences do exist, e.g., in scholarly productivity, postdoctoral training rates, and in career movements up the rankings of universities, suggesting that the effects of gender are indirectly incorporated into hiring decisions through gender's covariates. Furthermore, we find evidence that more highly ranked departments recruit female faculty at higher than expected rates, which appears to inhibit similar efforts by lower ranked departments. These findings illustrate the subtle nature of gender inequality in faculty hiring networks and provide new insights to the underrepresentation of women in computer science.

Gender, Productivity, and Prestige in Computer Science Faculty Hiring Networks
Samuel F. Way, Daniel B. Larremore, Aaron Clauset

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Selforganization in Complex Systems: The Past, Present, and Future of Synergetics (by Axel Pelster and Günter Wunner)

    This proceedings volume contains talks and poster presentations from the International Symposium "Self-Organization in Complex Systems: The Past, Present, and Future of Synergetics", which took place in Germany. The Symposium was organized in honour of Hermann Haken. With his fundamental theory of Synergetics he had laid the mathematical-physical basis for describing and analyzing self-organization processes in a diversity of fields of research. The quest for common and universal principles of self-organization in complex systems was clearly covered by the wide range of interdisciplinary topics reported during the Symposium. These extended from complexity in classical systems and quantum systems over self-organisation in neuroscience even to the physics of finance. Moreover, by combining a historical view with a present status report the Symposium conveyed an impression of the allure and potency of this branch of research as well as its applicability in the future.



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Emergent Nested Systems: A Theory of Understanding and Influencing Complex Systems as well as Case Studies in Urban Systems (by Christian Walloth)

    This book presents a theory as well as methods to understand and to purposively influence complex systems. It suggests a theory of complex systems as nested systems, i. e. systems that enclose other systems and that are simultaneously enclosed by even other systems. According to the theory presented, each enclosing system emerges through time from the generative activities of the systems they enclose.

Systems are nested and often emerge unplanned, and every system of high dynamics is enclosed by a system of slower dynamics. An understanding of systems with faster dynamics, which are always guided by systems of slower dynamics, opens up not only new ways to understanding systems, but also to effectively influence them.

The aim and subject of this book is to lay out these thoughts and explain their relevance to the purposive development of complex systems, which are exemplified in case studies from an urban system. The interested reader, who is not required to be familiar with system-theoretical concepts or with theories of emergence, will be guided through the development of a theory of emergent nested systems. The reader will also learn about new ways to influence the course of events - even though the course of events is, in principle, unpredictable, due to the ever-new emergence of real novelty.



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==============================================
Sponsored by the Complex Systems Society.
Founding Editor: Gottfried Mayer.
Editor-in-Chief: Carlos Gershenson.

You can contribute to Complexity Digest selecting one of our topics (http://unam.us4.list-manage1.com/track/click?u=0eb0ac9b4e8565f2967a8304b&id=07d782e762&e=55e25a0e3e ) and using the "Suggest" button.
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