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public:grl_readingmemo [2024/04/04 15:34] – [#01, liangz] liangpublic:grl_readingmemo [2024/04/04 16:12] (current) liang
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 p5: p5:
   * applications: bot detection in a social network, function of proteins in the interactome, classify the topic based on links, etc   * applications: bot detection in a social network, function of proteins in the interactome, classify the topic based on links, etc
-  * difference from a standard supervised learning+  * difference from a standard supervised learning: the assumption/bias of iid (independent and identically distributed) or no. 
 +  * popular inductive bias used in graph learning: homophily (same attrubute with neighbors), structural equivalence (similar local structure -> similar label), heterophily (e.g., gender). 
 + 
 +p6: 
 +  * supervised learning and semi-supervised learning, and GL (no iid assumption) 
 +  * relation prediction: e.g., recommendation system, side-effect. Notice the requirement of inductive bias. 
 + 
 +p7: 
 +  * clustering and community detection 
 +  * graph classification, regression, and clustering (to the audience: what is the general difference?
 + 
 +p8: 
 +  * iid assumption and why? -> Li-Yang 
 +  * Additional comment: Causal relation and correlation. ML is often consider the latter approach but actually we need to consider the former.
public/grl_readingmemo.1712212462.txt.gz · Last modified: 2024/04/04 15:34 by liang