Differences

This shows you the differences between two versions of the page.

--- public:grl_readingmemo [2024/04/04 06:34] – [#01, liangz] liang
+++ public:grl_readingmemo [2024/04/04 07:12] (current) – liang
@@ Line 61: / Line 61: @@
 p5:
   * 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.