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Showing posts older than #1020 · Back to latest

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Post #1017 703
📄Machine Learning for Refining Knowledge Graphs: A Survey

📘 Journal: acm digital library (I.F=14.324)
🗓Publish year: 2020

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey
  • 🔥 4
  • 👍 2
  • 👏 1
Post #1015 795
🎞 Graph Analytics and Graph-based Machine Learning

💥Free recorded course by Clair Sullivan(Neo4j)

💥Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.

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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning
YouTube Graph Analytics and Graph-based Machine Learning Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data…
  • 👍 4
  • 👏 1
Post #1011 1.13K
🎞 Machine Learning with Graphs: Generative Models for Graphs

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

💥In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.

📽 Watch

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Generative_Models
YouTube Stanford CS224W: Machine Learning with Graphs | 2021 | Lecture 14.1 - Generative Models for Graphs For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3jO8OsE Jure Leskovec Computer Science, PhD In this lecture, we will cover generative models for graphs. The goal of generative…
  • 👍 6
Post #1010 759
📄A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detection

🗓Publish year: 2023

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #GNN #Time_Series #Forecasting #Classification #Imputation #Anomaly_Detection #survey
  • 👍 4
Post #1008 824
📄A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations

📘 Journal: BRIEFINGS IN BIOINFORMATICS (I.F=10.6)
🗓Publish year: 2023

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #GNN #non_coding #RNA #complex_disease #review
  • 👍 3
Post #1007 807
📄Molecular networks in Network Medicine

📘 Journal: WILEY (I.F=5.609)
🗓Publish year: 2020

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Molecular_networks #Medicine
  • 👍 4
  • ❤ 1
Post #1006 857
📄A Survey on the Recent Advances of Deep Community Detection

📘 Journal: APPLIED SCIENCES-BASEL (I.F=2.7)
🗓Publish year: 2021

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Deep #Community_Detection #survey
  • 👍 4
Post #1005 6.89K
📄Network Medicine in Pathobiology

📘 journal: The American Journal of Pathology(I.F=5.1)
🗓Publish year: 2019

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Pathobiology #network #Medicine
  • 👍 6
Post #1004 1.06K
📄Graph neural networks for materials science and
chemistry

📘 Journal: Communications Materials (I.F=7.8)
🗓Publish year: 2022

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #GNN #materials_science #chemistry
  • 👍 2
Post #1003 2.15K
📄Generative Diffusion Models on Graphs: Methods and Applications

📘 CONFERENCE: INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI 2023)
🗓Publish year: 2023

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #Diffusion #Graph #Generative #DeepLearning
  • 👍 7
Post #1002 814
📄Social network research in the family business literature: a review and integration

📘 Journal: Small Business Economics (I.F=6.4)
🗓Publish year: 2023

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #research #family_business #literature #integration #review
  • 👏 2
Post #1001 1.44K
🎞 Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide

💥Free recorded course by Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin

💥Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural Networks (GNNs) are quickly becoming the de-facto Machine Learning models for learning from Graph data and hereby infer missing information, such as, predicting labels of nodes or imputing missing edges. The main goal of this tutorial is to help practitioners and researchers to implement GNNs in a TensorFlow setting. Specifically, the tutorial will be mostly hands-on, and will walk the audience through a process of running existing GNNs on heterogeneous graph data, and a tour of how to implement new GNN models. The hands-on portion of the tutorial will be based on TF-GNN, a new framework that we open-sourced.


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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #GNN #code #python #tensorflow
YouTube Tutorial: Graph Neural Networks in TensorFlow: A Practical Guide Organizers: Sami Abu-el-Haija, Neslihan Bulut, Bryan Perozzi, and Anton Tsitsulin Abstract: Graphs are general data structures that can represent information from a variety of domains (social, biomedical, online transactions, and many more). Graph Neural…
  • 👍 4
Post #1000 906
📄Recommending on graphs: a comprehensive review from a data perspective

📘 Journal: User Modeling and User-Adapted Interaction (I.F=5.7)
🗓Publish year: 2023

📎Study paper

📱Channel: @ComplexNetworkAnalysis
#paper #Recommending #perspective #review
  • ❤ 2
  • 👏 1
Post #999 909
🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.

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


📽 Watch

📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #DGNN #GNN
YouTube Stanford CS224W: ML with Graphs | 2021 | Lecture 15.4 - Applications of Deep Graph Generation For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3EwmakW Lecture 15.4: Application of Deep Graph Generative Models to Molecule Generation Jure Leskovec Computer Science, PhD Finally…
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  • ❤ 1
Post #998 901
🎞A Literature Review on Graph Neural Networks

💥Free recorded video on A Literature Review on Graph Neural Networks.

📄 first paper
📄 second paper

📽 Watch

📱Channel: @ComplexNetworkAnalysis

#video #GNN #review
YouTube A Literature Review on Graph Neural Networks For slides and more information on the paper, visit https://aisc.ai.science/events/2020-04-15-spotlight Discussion lead: Nabila Abraham Survey papers: 1. Graph Neural Networks: A Review of Methods and Applications https://arxiv.org/abs/1812.08434 2. Representation…
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Post #997 912
Network_and_Content_Analysis_in_an_Online_Community_Discourse.pdf292.2 KB
📕Network and Content Analysis in an Online Community Discourse

💥The aim of this paper is to study interaction patterns among the members of a community of practice within the Dutch police organization and the way they share and construct knowledge together. The online discourse between 46 members, using First Class, formed the basis for this study. Social Network Analysis and content analysis were used to analyze the data. The results show that the interaction patterns between the members are rather centralized and that the network is relatively dense. Most of the members are involved within the discourse but person to person communication is still rather high. Content analysis revealed that discourse is focused on sharing and comparing information.

🌐 Read online

📲Channel: @ComplexNetworkAnalysis

#book_Chapter
  • 👍 4
Post #996 817
📄Deep Learning on Graphs: A Survey

📘 Journal: IEEE Transactions on Knowledge and Data Engineering
🗓Publish year: 2022

📎Study paper

📲Channel: @ComplexNetworkAnalysis
#paper #GNN #Deep_learning #Survey
  • 👍 4
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