Network Analysis Resources & Updates – Telegram
Network Analysis Resources & Updates
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📄Complex network approaches to nonlinear time series analysis

📘Journal: Physics Reports (I.F=25.6)

🗓Publish year: 2019

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #time_series
🎞 Machine Learning with Graphs: Applications of Graph ML

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥Graph machine learning can be applied in many scenarios, including the tasks of node classification, link prediction, graph classification, etc. Machine Learning at different levels of graphs usually demonstrate powerful capability in many specific tasks in different fields, ranging from protein folding, drug discovery, to recommender system, traffic prediction, among various other tasks.


📽 Watch

📜 Slides

💻Codes: part1 part2

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #code #python
👍2
📄New perspectives on analysing data from biological collections based on social network analytics

📘
Journal: Scientific Reports (I.F=4.996)

🗓Publish year: 2020

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #biological
📄Applications of network analysis to routinely collected health care data: a systematic review

📘
Journal: Journal of the American Medical Informatics Association (I.F=7.942)

🗓Publish year: 2018

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Applications #health_care #review
🎓Analysis of the Structural Properties and Scalability of Complex Networks

📘A DISSERTATION SUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA

🗓Publish year: 2018

📎Study dissertation

📱Channel: @ComplexNetworkAnalysis
#dissertation #scalability
📄Multilayer Networks in a Nutshell

📘
Journal: Annual Review of Condensed Matter Physics (I.F=23.978)

🗓Publish year: 2018

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Multilayer_Networks
🎞 An Introduction to Social Network Analysis: Part 1

💥Free recorded workshop on Social Network Analysis (SNA)

💥 Part 1 of the workshop provides an introduction to social network concepts, theories, and substantive problems. A brief history of SNA is given

📽 Watch

📱Channel: @ComplexNetworkAnalysis

#video
📄Open Graph Benchmark: Datasets for Machine Learning on Graphs

💥Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

🗓Publish year: 2020

📎 Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Graphs
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📄Multilayer networks: aspects, implementations, and application in biomedicine

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Journal: Big Data Analytics

🗓Publish year: 2020

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #application #biomedicine
2018_Study_on_centrality_measures_in_social_networks_a_survey.pdf
1.2 MB
📄Study on centrality measures in social networks: a survey

📘Journal: Social Network Analysis and Mining (I.F=3.868)

🗓Publish year: 2018

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #survey #centrality
2015_Network_analysis_for_a_network_disorder_The_emerging_role_of.pdf
1.7 MB
📄Network analysis for a network disorder: The emerging role of graph theory in the study of epilepsy

📘Journal:Epilepsy & Behavior (I.F=2.937)

🗓Publish year: 2015

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #epilepsy
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📄Graph Theory in the Information Age

💥This article is based on the Noether Lecture given at the
AMS-MAA-SIAM Annual Meeting, January 2009, Washington D. C.

🗓Publish year: 2010

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper
📄Structure and tie strengths in mobile communication networks

📘Journal: PNAS (I.F=11.205)

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #mobile_communication
📄A Critical Review of Centrality Measures in Social Networks

📘Journal: Business & Information Systems Engineering (I.F=4.532)

🗓Publish year: 2010

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Social_Networks #Review #Centrality
🎞 Machine Learning with Graphs: Choice of Graph Representation

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥One essential task to consider before we conduct machine learning on graphs is to find an appropriate way to represent the graphs. What are the factors that will affect our choices as to the representations? In this video, we’ll be looking at the different approaches to abstracting graphs: directed vs. undirected, weighted vs. unweighted, homogeneous vs bipartite, and so on.


📽 Watch

📜 Slides

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning