Network Analysis Resources & Updates
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🎞 Introduction to Graph Computing

πŸ’₯Free recorded Lecture by Prof. Yadong Li

πŸ’₯Graph computing is an innovative technology that allows developers to build applications and systems as directed acyclic graphs (DAGs). Graph computing offers generic solutions to some of the most fundamental challenges in enterprise computing such as scalability, transparency and lineage. In this workshop, we survey the available graph computing tools in Julia, then walk through a few hands-on examples of building real world applications and systems using graph computing.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture #GraphComputing
πŸ“„Deep Learning for Community Detection: Progress, Challenges and Opportunities

πŸ“˜
Conference: Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}

πŸ—“Publish year: 2020

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection #DeepLearning
πŸ“„Graph neural networks: A review of methods and applications

πŸ“˜
Journal: AI Open

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #review #applications
πŸ“„Statistical Network Analysis: A Review with Applications to the Coronavirus Disease 2019 Pandemic

πŸ“˜
Journal: International Statistical Institute (I.F=1.946)

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #review #Applications #Coronavirus
πŸ“„A Review of Graph and Network Complexity from an Algorithmic Information Perspective

πŸ“˜
Journal: Entropy (I.F=2.738)

πŸ—“Publish year: 2018

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #review
πŸ“„Bipartite Graphs as Models of Complex Networks

πŸ—“Publish year: 2021

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
πŸ“„A Review on Graph Theory in Network and Artificial Intelligence

πŸ“˜
Conference: International Conference on Robotics and Artificial Intelligence (RoAI) 2020 28-29 December 2020, Chennai, India

πŸ—“Publish year: 2021

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #review #Artificial_Intelligence
🎞 Modeling epidemics on complex networks

πŸ’₯Free recorded Lecture in Department of Computer Science IIL Ropar

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture
πŸ“„Community Detection Methods in Social Network Analysis

πŸ“˜
Journal: Journal of Computational and Theoretical Nanoscience (I.F=0.488)

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #CommunityDetection
πŸ“„Implement Louvain Community Detection Algorithm using Python and Gephi with visualization

πŸ’₯
Technical paper

🌐 Study

πŸ“²Channel: @ComplexNetworkAnalysis

#paper #CommunityDetection #Gephi #Louvain #code #python
πŸ‘1
🎞 Introduction to Static Complex Networks

πŸ’₯Free recorded course by Professor Stephen Lansing

πŸ’₯This course explores the features of complexity science. Our world is connected by an abundance of complex systems. Across all levels of organizations from physical, biological world to the social world, we may think of the connectivity between individual elements and how they interact and influence each other. For example, how humans transmit pandemics within a group, how cars interact in the traffic system and how networks connect in governmental organizations. Although these systems are diverse and different, they have surprisingly huge features in common. In the past several decades, the study of complexity science has been increasing. It is widely acknowledged that an innovative, integrated and analytical way of thinking is essential for understanding the complex issues in the human societies. In this course, we will aim to give everyone a comprehensive introduction of the complex systems, to talk about the resilience, robustness and sustainability of the systems and to learn basic mathematical methods for complex system analysis, for example regime shifts and tipping points, the agent-based modelling, the dynamic and network theories. Most importantly, we will implement the theories into practical applications of cities and health to help students gain practice in complex systems way of thinking.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #course
πŸ“„Graph Neural Networks: a bibliometrics overview

πŸ“˜
Journal: Machine Learning with Applications (MLWA)

πŸ—“Publish year: 2022

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #overview
πŸ“„The Co-authorship Network of Published Articles in Conferences on Web Research Based on Social Network Analysis

πŸ“˜
Journal: International Journal on Web Research (IJWR)

πŸ—“Publish year: 2020

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #Co_authorship_Network
🎞 Complex Networks, Simple Rules

πŸ’₯Free recorded Lecture

πŸ’₯Complex networks are all around us, and they can be generated by simple mechanisms. Understanding what kinds of networks can be produced by following simple rules is therefore of great importance. We investigate this issue by studying the dynamics of extremely simple systems where are `writer' moves around a network, and modifies it in a way that depends upon the writer's surroundings. Each vertex in the network has three edges incident upon it, which are colored red, blue and green.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture
🎞 Complex networks of time-series: what does it reveal more than local interactions?

πŸ’₯Free recorded Lecture by Amirhossein Shirazi, IFISC (UIB-CSIC)

πŸ’₯There is a huge literature about extracting the interaction network of these systems in molecular biology, neuroscience and economy. Although this approach invigorates these disciplines to deal with large data, it usually focuses on microscopic results. In this presentation, I will suggest some holistic approaches towards the analysis of these networks, based on two examples: medical words network evolution and stock market network near the crisis. Finally, I will try to connect the measured global indicators to dynamics of the system, using the idea of symmetry breaking in the spin glass models.

πŸ“½ Watch

πŸ“²Channel: @ComplexNetworkAnalysis

#video #Lecture
πŸ“„Network Controllability Is Determined by the Density of Low In-Degree and Out-Degree Nodes

πŸ—“Publish year: 2014

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper #NetworkControllability
πŸ“„Characterizing cycle structure in complex networks

πŸ“˜
Journal: Communications Physics (I.F=6.497)

πŸ—“Publish year: 2021

πŸ“Ž Study the paper

πŸ“²Channel: @ComplexNetworkAnalysis
#paper
πŸ“„A guide to choosing and implementing reference models for social network analysis

πŸ“˜
Journal: BIOLOGICAL REVIEWS (I.F=14.35)

πŸ—“Publish year: 2021

πŸ“ŽStudy paper

πŸ“±Channel: @ComplexNetworkAnalysis
#paper #implementing