We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

Associate DIETRICH BELITZ, University of Oregon Editors: Condensed Matter Physics (Theoretical) J. IGNACIO CIRAC, Max-Planck-Institut für Quantenoptik Quantum Information RAYMOND E. GOLDSTEIN, University of Cambridge Biological Physics ARTHUR F. HEBARD, University of Florida Condensed Matter Physics (Experimental) RANDALL D. KAMIEN, University of Pennsylvania Soft Condensed Matter DANIEL KLEPPNER, Massachusetts Institute of Technology Atomic, Molecular, and Optical Physics (Experimental) PAUL G. LANGACKER, Institute for Advanced Study, Princeton University Particle Physics (Theoretical) VERA LÜTH, Stanford University Particle Physics (Experimental) DAVID D. MEYERHOFER, University of Rochester Physics of Plasmas and Matter at High-Energy Density WITOLD NAZAREWICZ, University of Tennessee, Oak Ridge National Laboratory Nuclear Physics JOHN H. SCHWARZ, California Institute of Technology Mathematical Physics FRIEDRICH-KARL THIELEMANN, Universität Basel Astrophysics Senior Assistant Editor: DEBBIE BRODBAR, APS Editorial Office American Physical Society

Reviews of Modern Physics

Every organisation from the scale of whole countries down to small companies has a list of system developments which have ended in various forms of disaster. The nature of the failures varies but typical examples are: cost overruns; timescale overruns and sometimes, loss of life. The post-mortems to these systems reveal a wide range of reasons all the way from hardware failures, through software errors right to major system level mistakes. More importantly a large number of these systems share one attribute: complexity. This paper presents a fresh look at the nature of complexity in the building of computer based systems.

http://ieeexplore.ieee.org/iel3/4458/12639/00581896.pdf

Complex systems

Social media data has become crucial to the advancement of scientific understanding. However, even though it has become ubiquitous, just collecting large-scale social media data involves a high degree of engineering skill set and computational resources. In fact, research is often times gated by data engineering problems that must be overcome before analysis can proceed. This has resulted recognition of datasets as meaningful research contributions in and of themselves. Reddit, the so called "front page of the Internet," in particular has been the subject of numerous scientific studies. Although Reddit is relatively open to data acquisition compared to social media platforms like Facebook and Twitter, the technical barriers to acquisition still remain. Thus, Reddit's millions of subreddits, hundreds of millions of users, and hundreds of billions of comments are at the same time relatively accessible, but time consuming to collect and analyze systematically. In this paper, we present the Pushshift Reddit dataset. Pushshift is a social media data collection, analysis, and archiving platform that since 2015 has collected Reddit data and made it available to researchers. Pushshift's Reddit dataset is updated in real-time, and includes historical data back to Reddit's inception. In addition to monthly dumps, Pushshift provides computational tools to aid in searching, aggregating, and performing exploratory analysis on the entirety of the dataset. The Pushshift Reddit dataset makes it possible for social media researchers to reduce time spent in the data collection, cleaning, and storage phases of their projects.

The Pushshift Reddit Dataset

Thank you for downloading radiation effects in solids. Maybe you have knowledge that, people have look hundreds times for their chosen novels like this radiation effects in solids, but end up in harmful downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their laptop. radiation effects in solids is available in our digital library an online access to it is set as public so you can get it instantly. Our books collection spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the radiation effects in solids is universally compatible with any devices to read.

Radiation Effects In Solids

Hashkat (this http URL) is a free, open source, agent based simulation software package designed to simulate large-scale online social networks (e.g. Twitter, Facebook, LinkedIn, etc). It allows for dynamic agent generation, edge creation, and information propagation. The purpose of hashkat is to study the growth of online social networks and how information flows within them. Like real life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics. Hashkat was implemented in C++, and was designed with extensibility in mind. The software includes Shell and Python scripts for easy installation and usability. In this report, we describe all of the algorithms and features integrated into hashkat before moving on to example use cases. In general, hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, develop business strategy for advertising on online social networks, and test new features of an online social network before going into production.

Hashkat: Large-scale simulations of online social networks

Hashkat (
 http://hashkatorg
 ) is a free, open source, agent-based simulation software package designed to simulate large-scale online social networks (eg, Twitter, Facebook, LinkedIn) It allows for dynamic agent generation, edge creation, and information propagation The purpose of hashkat is to study the growth of online social networks and how information flows within them Like real-life online social networks, hashkat incorporates user relationships, information diffusion, and trending topics Hashkat was implemented in C++ and was designed with extensibility in mind The software includes Bash and Python scripts for easy installation and usability In this report, we describe all of the algorithms and features integrated into hashkat before moving on to example use cases In general, hashkat can be used to understand the underlying topology of social networks, validate sampling methods of such networks, develop business strategy for advertising on online social networks, and test new features of an online social network before going into production

Hashkat: large-scale simulations of online social networks

Using comments made in discussion threads on the social media aggregation website Reddit, the topics of conversation were identified using the probabilistic topic model Latent Dirichlet Allocation (LDA). Employing these topics as features for a neural network, several different neural network frameworks were trained on the data to serve as models to identify which threads a given user would be interested in contributing to based on their previously shown interests. This was done on a sample set of 30 users using 10 different initial random weights for each framework. The ideal model for each user was identified as being the one that scored the highest F2-Score, the harmonic mean of precision and recall with a bias towards recall, on a development set. Testing these ideal models on a test set, they achieved an average F2-Score of 0.825, as well as an average precision of 0.542 and an average recall of 0.956 for a sample set of users.

Nicholas Buhagiar

Papers

Hashkat: Large-scale simulations of online social networks

Hashkat: large-scale simulations of online social networks

Using Deep Learning to Recommend Discussion Threads to Users in an Online Forum